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is a machine learning pipeline for predicting successful cryptocurrency tokens on the PumpFun platform using the CatBoost algorithm. The code processes financial time-series data, engineers sophisticated features, trains a classification model, and generates predictions.\n1. Imports and Initialization\npython\n\nimport pandas as pd\nimport numpy as np\nfrom catboost import CatBoostClassifier, Pool\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report, jaccard_score\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\nimport warnings\nwarnings.filterwarnings('ignore')\n\nKey Libraries:\n\n    CatBoost: Gradient boosting algorithm that handles categorical features natively\n\n    scikit-learn: For data splitting and evaluation metrics\n\n    matplotlib/seaborn: For visualization\n\n    pandas/numpy: Data manipulation\n\n2. Main Class: PumpFunTokenPredictor\nConstructor\npython\n\ndef __init__(self, root_path):\n    self.root_path = root_path\n    self.model = None\n    self.feature_columns = []\n    self.categorical_features = []\n\nStores the data path and initializes empty containers for the model and feature metadata.\n3. Data Loading\nload_data() Method\n\n    Loads 5 evaluation dataset chunks and a sample dataset for training\n\n    Combines all evaluation chunks into one DataFrame\n\n    Provides shape information for debugging\n\nPurpose: In a real competition scenario, you'd have separate training and evaluation sets.\n4. Data Preprocessing\nsafe_timestamp_conversion() Method\n\nProblem: Handles malformed timestamps like \"26:28.7\" (invalid hour 26)\nSolution:\n\n    Detects invalid hours (>23) and normalizes them using modulo 24\n\n    Adds dummy dates if only time is provided\n\n    Provides fallback timestamps for parsing failures\n\nsafe_column_operation() Method\n\nSafety Mechanism: Gracefully handles missing columns by returning default values, preventing pipeline crashes.\n5. Feature Engineering - The Core Intelligence\n\nThe engineer_features() method creates 25+ sophisticated financial indicators:\nPrice and Momentum Features\n\n    price_momentum: Combines rate of change and momentum\n\n    volume_price_trend: Interaction between volume and price movement\n\nLiquidity and Market Features\n\n    liquidity_depth: Ratio of SOL to token reserves\n\n    market_depth_ratio: Market cap relative to volume\n\nHolder Analysis\n\n    holder_concentration: Top 10% holders' share\n\n    holder_growth_rate: Current vs total holders ratio\n\nTransaction Analysis\n\n    tx_intensity: Transaction count per volume unit\n\n    buy_pressure/sell_pressure: Buy/sell ratios\n\nTechnical Indicators\n\n    rsi_mfi_sync: Combines RSI and Money Flow Index\n\n    bollinger_volatility: Bollinger bands with volume oscillator\n\nRisk and Efficiency Metrics\n\n    risk_adjusted_return: Returns normalized by volume\n\n    gas_efficiency: Volume per gas consumed\n\nTime-Based Features\n\n    time_decay_factor: Exponential decay based on minute within 30s window\n\n6. Target Variable Creation\ncreate_target_variable() Method\n\nCreates a composite scoring system to identify successful tokens:\n\nScoring Components:\n\n    Price Score (25%): Tanh-scaled rate of change\n\n    Volume Score (20%): Volume relative to 75th percentile\n\n    Holder Score (15%): Tanh-scaled holder count\n\n    Liquidity Score (15%): Inverse liquidity ratio\n\n    Technical Score (25%): Binary indicators from multiple technical signals\n\nThreshold: Top 20% of composite scores become positive class (target tokens)\n7. Feature Selection\nselect_features() Method\n\n    Excludes identifier columns (timestamps, IDs)\n\n    Intelligently identifies categorical features: Only string columns with 2-50 unique values\n\n    Prevents high-cardinality columns from being treated as categorical\n\n8. Model Training with CatBoost\nKey Configuration:\npython\n\nCatBoostClassifier(\n    iterations=2000,           # Maximum trees\n    learning_rate=0.05,        # Step size\n    depth=8,                   # Tree depth\n    l2_leaf_reg=3,            # Regularization\n    border_count=128,          # Split points for numerical features\n    loss_function='Logloss',   # Binary classification\n    eval_metric='F1',          # Optimization metric\n    early_stopping_rounds=100, # Prevent overfitting\n)\n\nAdvanced Feature: Uses Pool objects with categorical feature indices for optimal performance.\n9. Comprehensive Model Evaluation\nMetrics Tracked:\n\n    Jaccard Index (IoU): Intersection over Union for binary classification\n\n    Confusion Matrix: Visualized with seaborn heatmap\n\n    Classification Report: Precision, recall, F1-score\n\n    Feature Importance: Top 20 most influential features\n\n10. Submission Generation\ncreate_submission() Method:\n\n    Processes the evaluation dataset through the same feature engineering pipeline\n\n    Generates binary predictions and probabilities\n\n    Creates two output files:\n\n        submission.csv: Minimal format for competition submission\n\n        detailed_predictions.csv: Full predictions with probabilities\n\n11. Visualization Components\nMultiple plots generated:\n\n    Confusion Matrix Heatmap\n\n    Feature Importance Bar Chart\n\n    Training Curves (loss and F1-score evolution)\n\n    Prediction Distributions (final submission analysis)\n\n12. Main Execution Flow\npython\n\ndef main():\n    # Initialize → Load Data → Engineer Features → Create Target\n    # → Select Features → Train/Validate → Evaluate → Generate Submission\n\nKey Technical Innovations\n1. Robust Data Handling\n\n    Graceful handling of malformed data\n\n    Safe column access patterns\n\n    Comprehensive missing value imputation\n\n2. Sophisticated Feature Engineering\n\n    Domain-specific financial indicators\n\n    Interaction terms between related metrics\n\n    Time-series aware features\n\n3. Composite Target Definition\n\n    Multi-faceted success scoring\n\n    Balanced weighting of different success factors\n\n    Adaptive thresholding\n\n4. Production-Ready Pipeline\n\n    Reproducible data processing\n\n    Comprehensive evaluation suite\n\n    Detailed logging and visualization\n\nBusiness Context\n\nThis system appears designed for a crypto trading competition where participants predict which newly launched tokens will be successful. The \"success\" is defined by price appreciation, trading volume, holder growth, and technical indicators within a short time window (likely 30 seconds based on the dataset names).\n\nThe model helps identify promising tokens early by analyzing:\n\n    Market microstructure\n\n    Holder behavior\n\n    Liquidity patterns\n\n    Technical indicators\n\n    Creator activity\n\nThis represents a sophisticated approach to quantitative trading in the highly volatile cryptocurrency market.","metadata":{"id":"MAkyGzxS21Cb"}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom catboost import CatBoostClassifier, Pool\nfrom sklearn.model_selection import train_test_split, StratifiedKFold\nfrom sklearn.metrics import confusion_matrix, classification_report, jaccard_score\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\nimport warnings\nwarnings.filterwarnings('ignore')\n\nclass PumpFunTokenPredictor:\n    def __init__(self, root_path, september_path):\n        self.root_path = root_path\n        self.september_path = september_path\n        self.model = None\n        self.feature_columns = []\n        self.categorical_features = []\n\n    def load_data(self):\n        \"\"\"Load and combine all dataset files including September 2025 data\"\"\"\n        print(\"Loading dataset files...\")\n\n        # Load evaluation chunks\n        eval_files = [\n            f\"{self.root_path}/evaluation_set_30s_chunk_001.csv\",\n            f\"{self.root_path}/evaluation_set_30s_chunk_002.csv\",\n            f\"{self.root_path}/evaluation_set_30s_chunk_003.csv\",\n            f\"{self.root_path}/evaluation_set_30s_chunk_004.csv\",\n            f\"{self.root_path}/evaluation_set_30s_chunk_005.csv\"\n        ]\n\n        # Load September 2025 chunks\n        september_files = [\n            f\"{self.september_path}/september_2025_first30s_chunk_001.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_002.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_003.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_004.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_005.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_006.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_007.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_008.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_009.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_010.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_011.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_012.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_013.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_014.csv\",\n            f\"{self.september_path}/september_2025_first30s_chunk_015.csv\"\n        ]\n\n        # Load sample dataset for training reference\n        sample_df = pd.read_csv(f\"{self.root_path}/Sample_Dataset.csv\")\n\n        # Combine September 2025 chunks for additional training data\n        september_dfs = []\n        for file in september_files:\n            try:\n                df = pd.read_csv(file)\n                september_dfs.append(df)\n                print(f\"Loaded {file.split('/')[-1]} with shape {df.shape}\")\n            except Exception as e:\n                print(f\"Error loading {file}: {e}\")\n\n        combined_september_df = pd.concat(september_dfs, ignore_index=True)\n        print(f\"Combined September 2025 dataset shape: {combined_september_df.shape}\")\n\n        # Combine all evaluation chunks\n        eval_dfs = []\n        for file in eval_files:\n            try:\n                df = pd.read_csv(file)\n                eval_dfs.append(df)\n                print(f\"Loaded {file.split('/')[-1]} with shape {df.shape}\")\n            except Exception as e:\n                print(f\"Error loading {file}: {e}\")\n\n        combined_eval_df = pd.concat(eval_dfs, ignore_index=True)\n        print(f\"Combined evaluation dataset shape: {combined_eval_df.shape}\")\n\n        # Combine sample and September data for enhanced training\n        combined_training_df = pd.concat([sample_df, combined_september_df], ignore_index=True)\n        print(f\"Combined training dataset shape: {combined_training_df.shape}\")\n\n        return combined_training_df, combined_eval_df\n\n    def perform_eda(self, df, title=\"Dataset EDA\"):\n        \"\"\"Comprehensive Exploratory Data Analysis\"\"\"\n        print(f\"\\n{'='*60}\")\n        print(f\"{title}\")\n        print(f\"{'='*60}\")\n        \n        # Basic statistics\n        print(f\"\\nDataset Shape: {df.shape}\")\n        print(f\"Unique Tokens: {df['mint_token_id'].nunique()}\")\n        print(f\"Date Range: {df['timestamp'].min()} to {df['timestamp'].max()}\")\n        \n        # Missing values\n        missing = df.isnull().sum()\n        if missing.sum() > 0:\n            print(\"\\nMissing Values:\")\n            print(missing[missing > 0])\n        \n        # Create visualization\n        fig = plt.figure(figsize=(20, 12))\n        \n        # Trade mode distribution\n        plt.subplot(3, 4, 1)\n        df['trade_mode'].value_counts().plot(kind='bar', color=['green', 'red'])\n        plt.title('Trade Mode Distribution')\n        plt.xlabel('Trade Mode')\n        plt.ylabel('Count')\n        \n        # Market cap distribution\n        plt.subplot(3, 4, 2)\n        plt.hist(df['market_cap_usd'].clip(upper=df['market_cap_usd'].quantile(0.95)), \n                 bins=50, edgecolor='black')\n        plt.title('Market Cap Distribution (95th percentile)')\n        plt.xlabel('Market Cap (USD)')\n        plt.ylabel('Frequency')\n        \n        # Volume analysis\n        plt.subplot(3, 4, 3)\n        plt.hist(df['sol_volume'].clip(upper=df['sol_volume'].quantile(0.95)), \n                 bins=50, edgecolor='black', color='orange')\n        plt.title('SOL Volume Distribution (95th percentile)')\n        plt.xlabel('SOL Volume')\n        plt.ylabel('Frequency')\n        \n        # Buy vs Sell ratio\n        plt.subplot(3, 4, 4)\n        buy_sell_data = df.groupby('mint_token_id').agg({\n            'buy_count': 'sum',\n            'sell_count': 'sum'\n        }).reset_index()\n        plt.scatter(buy_sell_data['buy_count'], buy_sell_data['sell_count'], \n                   alpha=0.5, s=10)\n        plt.title('Buy Count vs Sell Count per Token')\n        plt.xlabel('Buy Count')\n        plt.ylabel('Sell Count')\n        \n        # Holder statistics\n        plt.subplot(3, 4, 5)\n        plt.hist(df['current_holders'].clip(upper=df['current_holders'].quantile(0.95)), \n                 bins=30, edgecolor='black', color='purple')\n        plt.title('Current Holders Distribution')\n        plt.xlabel('Current Holders')\n        plt.ylabel('Frequency')\n        \n        # RSI distribution\n        plt.subplot(3, 4, 6)\n        plt.hist(df['relative_strength_index'], bins=50, edgecolor='black', color='teal')\n        plt.title('RSI Distribution')\n        plt.xlabel('RSI')\n        plt.ylabel('Frequency')\n        \n        # Rate of change\n        plt.subplot(3, 4, 7)\n        plt.hist(df['rate_of_change'].clip(lower=df['rate_of_change'].quantile(0.05),\n                                           upper=df['rate_of_change'].quantile(0.95)), \n                 bins=50, edgecolor='black', color='brown')\n        plt.title('Rate of Change Distribution (5-95th percentile)')\n        plt.xlabel('Rate of Change')\n        plt.ylabel('Frequency')\n        \n        # Creator sold analysis\n        plt.subplot(3, 4, 8)\n        df['creator_sold'].value_counts().plot(kind='bar', color=['blue', 'red'])\n        plt.title('Creator Sold Status')\n        plt.xlabel('Creator Sold')\n        plt.ylabel('Count')\n        \n        # Liquidity ratio\n        plt.subplot(3, 4, 9)\n        plt.hist(np.log10(df['liquidity_ratio'] + 1e-10), bins=50, edgecolor='black', color='cyan')\n        plt.title('Liquidity Ratio (log scale)')\n        plt.xlabel('Log10(Liquidity Ratio)')\n        plt.ylabel('Frequency')\n        \n        # Top 10 holder concentration\n        plt.subplot(3, 4, 10)\n        plt.hist(df['top10_percent_total'].clip(upper=df['top10_percent_total'].quantile(0.95)), \n                 bins=50, edgecolor='black', color='magenta')\n        plt.title('Top 10 Holder Concentration')\n        plt.xlabel('Top 10 %')\n        plt.ylabel('Frequency')\n        \n        # Bollinger position\n        plt.subplot(3, 4, 11)\n        plt.hist(df['bollinger_relative_position'], bins=50, edgecolor='black', color='olive')\n        plt.title('Bollinger Relative Position')\n        plt.xlabel('Position')\n        plt.ylabel('Frequency')\n        \n        # Money Flow Index\n        plt.subplot(3, 4, 12)\n        plt.hist(df['money_flow_index'], bins=50, edgecolor='black', color='pink')\n        plt.title('Money Flow Index Distribution')\n        plt.xlabel('MFI')\n        plt.ylabel('Frequency')\n        \n        plt.tight_layout()\n        plt.show()\n        \n        # Correlation heatmap for key features\n        print(\"\\nGenerating correlation heatmap...\")\n        numeric_cols = ['market_cap_usd', 'sol_volume', 'token_volume', 'buy_count', \n                       'sell_count', 'current_holders', 'relative_strength_index', \n                       'rate_of_change', 'liquidity_ratio', 'buy_sell_ratio']\n        \n        available_cols = [col for col in numeric_cols if col in df.columns]\n        \n        if len(available_cols) > 0:\n            fig, ax = plt.subplots(figsize=(12, 10))\n            correlation_matrix = df[available_cols].corr()\n            sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', \n                       center=0, square=True, ax=ax)\n            plt.title('Feature Correlation Heatmap')\n            plt.tight_layout()\n            plt.show()\n\n    def safe_timestamp_conversion(self, timestamp_series):\n        \"\"\"Safely convert timestamp series handling invalid hour values\"\"\"\n        converted_timestamps = []\n\n        for ts in timestamp_series:\n            try:\n                # Try direct conversion first\n                if isinstance(ts, str):\n                    # Handle the specific case of \"26:28.7\" format\n                    if ':' in ts and len(ts.split(':')) > 1:\n                        parts = ts.split(':')\n                        if len(parts) >= 2:\n                            hour_part = parts[0]\n                            # Check if hour is invalid (>23)\n                            if hour_part.isdigit() and int(hour_part) >= 24:\n                                # Normalize the hour (wrap around)\n                                normalized_hour = int(hour_part) % 24\n                                remaining_parts = ':'.join(parts[1:])\n                                normalized_ts = f\"{normalized_hour:02d}:{remaining_parts}\"\n                                # Try to create a proper datetime\n                                # If it's just time without date, add a dummy date\n                                if ' ' not in normalized_ts:\n                                    normalized_ts = f\"2024-01-01 {normalized_ts}\"\n                                converted_ts = pd.to_datetime(normalized_ts)\n                            else:\n                                converted_ts = pd.to_datetime(ts)\n                        else:\n                            converted_ts = pd.to_datetime(ts)\n                    else:\n                        converted_ts = pd.to_datetime(ts)\n                else:\n                    converted_ts = pd.to_datetime(ts)\n                converted_timestamps.append(converted_ts)\n            except Exception as e:\n                # Fallback: use current timestamp or a default\n                print(f\"Warning: Could not parse timestamp {ts}: {e}\")\n                converted_timestamps.append(pd.Timestamp('2024-01-01'))  # Default fallback\n\n        return pd.Series(converted_timestamps, index=timestamp_series.index)\n\n    def safe_column_operation(self, df, col_name, default_value=0):\n        \"\"\"Safely access a column, return default value if column doesn't exist\"\"\"\n        if col_name in df.columns:\n            return df[col_name]\n        else:\n            print(f\"Warning: Column '{col_name}' not found, using default value {default_value}\")\n            return pd.Series([default_value] * len(df), index=df.index)\n\n    def engineer_features(self, df):\n        \"\"\"Engineer sophisticated features from the raw data\"\"\"\n        print(\"Engineering features...\")\n\n        # Create a copy to avoid modifying original\n        features_df = df.copy()\n\n        # Convert timestamp to datetime using safe conversion\n        print(\"Converting timestamps...\")\n        features_df['timestamp'] = self.safe_timestamp_conversion(features_df['timestamp'])\n\n        # Extract time-based features from valid timestamps\n        features_df['hour'] = features_df['timestamp'].dt.hour\n        features_df['day_of_week'] = features_df['timestamp'].dt.dayofweek\n        features_df['minute'] = features_df['timestamp'].dt.minute\n\n        # Use safe column access for all feature engineering operations\n        rate_of_change = self.safe_column_operation(features_df, 'rate_of_change', 0)\n        momentum = self.safe_column_operation(features_df, 'momentum', 0)\n        sol_volume = self.safe_column_operation(features_df, 'sol_volume', 0)\n        virtual_sol_reserves = self.safe_column_operation(features_df, 'virtual_sol_reserves', 1)\n        virtual_token_reserves = self.safe_column_operation(features_df, 'virtual_token_reserves', 1)\n        market_cap_usd = self.safe_column_operation(features_df, 'market_cap_usd', 1)\n        top10_percent_total = self.safe_column_operation(features_df, 'top10_percent_total', 0)\n        current_holders = self.safe_column_operation(features_df, 'current_holders', 1)\n        total_holders = self.safe_column_operation(features_df, 'total_holders', 1)\n        total_count = self.safe_column_operation(features_df, 'total_count', 1)\n        buy_count = self.safe_column_operation(features_df, 'buy_count', 0)\n        sell_count = self.safe_column_operation(features_df, 'sell_count', 0)\n        creator_balance = self.safe_column_operation(features_df, 'creator_balance', 0)\n        creator_sold = self.safe_column_operation(features_df, 'creator_sold', 0)\n        relative_strength_index = self.safe_column_operation(features_df, 'relative_strength_index', 50)\n        money_flow_index = self.safe_column_operation(features_df, 'money_flow_index', 50)\n        bollinger_relative_position = self.safe_column_operation(features_df, 'bollinger_relative_position', 0)\n        volume_oscillator = self.safe_column_operation(features_df, 'volume_oscillator', 0)\n        liquidity_ratio = self.safe_column_operation(features_df, 'liquidity_ratio', 0)\n        buy_sell_ratio = self.safe_column_operation(features_df, 'buy_sell_ratio', 1)\n        holder_ratio = self.safe_column_operation(features_df, 'holder_ratio', 0)\n        token_volume = self.safe_column_operation(features_df, 'token_volume', 0)\n        consumed_gas = self.safe_column_operation(features_df, 'consumed_gas', 1)\n        fee = self.safe_column_operation(features_df, 'fee', 0)\n\n        # Price and volume momentum features\n        features_df['price_momentum'] = rate_of_change * momentum\n        features_df['volume_price_trend'] = sol_volume * rate_of_change\n\n        # Liquidity and market depth features\n        features_df['liquidity_depth'] = virtual_sol_reserves / (virtual_token_reserves + 1)\n        features_df['market_depth_ratio'] = market_cap_usd / (sol_volume + 1)\n\n        # Holder concentration features\n        features_df['holder_concentration'] = top10_percent_total / (current_holders + 1)\n        features_df['holder_growth_rate'] = current_holders / (total_holders + 1)\n\n        # Transaction intensity features\n        features_df['tx_intensity'] = total_count / (sol_volume + 1)\n        features_df['buy_pressure'] = buy_count / (total_count + 1)\n        features_df['sell_pressure'] = sell_count / (total_count + 1)\n\n        # Creator behavior features\n        features_df['creator_engagement'] = creator_balance / (market_cap_usd + 1)\n        features_df['creator_sell_ratio'] = creator_sold.astype(int) * creator_balance\n\n        # Technical indicator combinations\n        features_df['rsi_mfi_sync'] = (relative_strength_index - 50) * (money_flow_index - 50)\n        features_df['bollinger_volatility'] = bollinger_relative_position * volume_oscillator\n\n        # Risk-adjusted features\n        features_df['risk_adjusted_return'] = rate_of_change / (sol_volume + 1)\n        features_df['volatility_efficiency'] = rate_of_change / (volume_oscillator.abs() + 1)\n\n        # Network effect features\n        features_df['network_effect'] = current_holders * buy_sell_ratio\n        features_df['community_strength'] = holder_ratio * features_df['buy_pressure']\n\n        # Gas and fee efficiency\n        features_df['gas_efficiency'] = sol_volume / (consumed_gas + 1)\n        features_df['fee_to_volume_ratio'] = fee / (sol_volume + 1)\n\n        # Market microstructure features\n        features_df['order_imbalance'] = (buy_count - sell_count) / (total_count + 1)\n        features_df['volume_imbalance'] = (token_volume * features_df['buy_pressure']) / (sol_volume + 1)\n\n        # Time decay features (within 30-second window)\n        features_df['time_decay_factor'] = np.exp(-features_df['minute'] / 30)\n\n        print(f\"Original features: {len(df.columns)}, Engineered features: {len(features_df.columns)}\")\n        return features_df\n\n    def create_target_variable(self, df, strategy='composite'):\n        \"\"\"\n        Create target variable using sophisticated composite scoring\n        In real scenario, this would use the actual target token list\n        \"\"\"\n        print(\"Creating target variable...\")\n\n        # Create composite score based on multiple success indicators\n        df = df.copy()\n\n        # Use safe column access for target creation\n        rate_of_change = self.safe_column_operation(df, 'rate_of_change', 0)\n        sol_volume = self.safe_column_operation(df, 'sol_volume', 0)\n        current_holders = self.safe_column_operation(df, 'current_holders', 0)\n        liquidity_ratio = self.safe_column_operation(df, 'liquidity_ratio', 0)\n        relative_strength_index = self.safe_column_operation(df, 'relative_strength_index', 50)\n        money_flow_index = self.safe_column_operation(df, 'money_flow_index', 50)\n        bollinger_relative_position = self.safe_column_operation(df, 'bollinger_relative_position', 50)\n        buy_sell_ratio = self.safe_column_operation(df, 'buy_sell_ratio', 1)\n        volume_oscillator = self.safe_column_operation(df, 'volume_oscillator', 0)\n\n        # Price momentum score\n        price_score = np.tanh(rate_of_change * 10)\n\n        # Volume strength score\n        volume_quantile = sol_volume.quantile(0.75) if sol_volume.quantile(0.75) > 0 else 1\n        volume_score = np.tanh(sol_volume / volume_quantile)\n\n        # Holder growth score\n        holder_score = np.tanh(current_holders / 10)\n\n        # Liquidity quality score\n        liquidity_score = 1 - np.tanh(liquidity_ratio * 1e7)\n\n        # Technical indicator score\n        tech_score = (\n            (relative_strength_index > 60).astype(int) * 0.2 +\n            (money_flow_index > 60).astype(int) * 0.2 +\n            (bollinger_relative_position > 60).astype(int) * 0.2 +\n            (buy_sell_ratio > 1.5).astype(int) * 0.2 +\n            (volume_oscillator > 0).astype(int) * 0.2\n        )\n\n        # Composite target score\n        composite_score = (\n            price_score * 0.25 +\n            volume_score * 0.20 +\n            holder_score * 0.15 +\n            liquidity_score * 0.15 +\n            tech_score * 0.25\n        )\n\n        # Create binary target (top 20% as positive class)\n        threshold = composite_score.quantile(0.80) if len(composite_score) > 0 else 0.5\n        df['is_target'] = (composite_score >= threshold).astype(int)\n\n        target_ratio = df['is_target'].mean()\n        print(f\"Target class distribution: {target_ratio:.3f} ({df['is_target'].sum()} positive samples)\")\n\n        return df\n\n    def select_features(self, df):\n        \"\"\"Select final feature set for model training\"\"\"\n        # Remove identifier and target columns\n        exclude_cols = ['timestamp', 'mint_token_id', 'holder', 'creator', 'is_target', 'index']\n\n        # Get all available columns\n        all_columns = [col for col in df.columns if col not in exclude_cols]\n\n        # Identify categorical features - FIXED: Only truly categorical columns\n        categorical_features = []\n        for col in all_columns:\n            # Only consider string/object type columns as categorical\n            # AND ensure they have reasonable number of categories (not IDs)\n            if df[col].dtype == 'object':\n                n_unique = df[col].nunique()\n                # Only categorical if it's a string column with reasonable cardinality\n                if 1 < n_unique < 50:  # More restrictive threshold\n                    categorical_features.append(col)\n                    print(f\"Categorical feature detected: {col} with {n_unique} unique values\")\n\n        # All other columns are numerical\n        numerical_features = [col for col in all_columns if col not in categorical_features]\n\n        print(f\"Selected {len(numerical_features)} numerical features and {len(categorical_features)} categorical features\")\n\n        self.feature_columns = numerical_features + categorical_features\n        self.categorical_features = categorical_features\n\n        return numerical_features, categorical_features\n\n    def prepare_model_data(self, df):\n        \"\"\"Prepare data for model training\"\"\"\n        X = df[self.feature_columns].copy()\n        y = df['is_target'].copy()\n\n        # Handle missing values and ensure correct dtypes\n        for col in X.columns:\n            if col in self.categorical_features:\n                # Convert categorical to string explicitly\n                X[col] = X[col].astype(str)\n                if X[col].isnull().any():\n                    X[col].fillna('missing', inplace=True)\n            else:\n                # Ensure numerical columns are float\n                X[col] = pd.to_numeric(X[col], errors='coerce')\n                if X[col].isnull().any():\n                    X[col].fillna(X[col].median(), inplace=True)\n\n        return X, y\n\n    def train_model(self, X_train, y_train, X_val=None, y_val=None):\n        \"\"\"Train CatBoost model with sophisticated parameter tuning\"\"\"\n        print(\"Training CatBoost model...\")\n\n        # Get categorical feature indices\n        cat_feature_indices = [X_train.columns.get_loc(col) for col in self.categorical_features if col in X_train.columns]\n\n        print(f\"Using {len(cat_feature_indices)} categorical features at indices: {cat_feature_indices}\")\n\n        # Create catboost pool with indices instead of names\n        train_pool = Pool(\n            X_train,\n            y_train,\n            cat_features=cat_feature_indices  # Use indices instead of column names\n        )\n\n        val_pool = None\n        if X_val is not None and y_val is not None:\n            val_pool = Pool(\n                X_val,\n                y_val,\n                cat_features=cat_feature_indices  # Use indices instead of column names\n            )\n\n        # Initialize CatBoost with optimized parameters\n        self.model = CatBoostClassifier(\n            iterations=2000,\n            learning_rate=0.05,\n            depth=8,\n            l2_leaf_reg=3,\n            border_count=128,\n            loss_function='Logloss',\n            eval_metric='F1',\n            random_seed=42,\n            early_stopping_rounds=100,\n            verbose=100,\n            use_best_model=True\n        )\n\n        # Train model\n        if val_pool is not None:\n            self.model.fit(\n                train_pool,\n                eval_set=val_pool,\n                plot=True,\n                verbose=True\n            )\n        else:\n            self.model.fit(\n                train_pool,\n                plot=True,\n                verbose=True\n            )\n\n        print(\"Model training completed!\")\n        return self.model\n\n    def evaluate_model_with_jaccard(self, X_test, y_test):\n        \"\"\"Comprehensive model evaluation with Jaccard Index focus\"\"\"\n        print(\"\\n\" + \"=\"*50)\n        print(\"MODEL EVALUATION - JACCARD INDEX\")\n        print(\"=\"*50)\n\n        y_pred = self.model.predict(X_test)\n        y_pred_proba = self.model.predict_proba(X_test)[:, 1]\n\n        # Calculate Jaccard Index (Intersection-over-Union)\n        jaccard = jaccard_score(y_test, y_pred)\n\n        # Confusion matrix\n        cm = confusion_matrix(y_test, y_pred)\n\n        # Classification report\n        cr = classification_report(y_test, y_pred, output_dict=True)\n\n        print(f\"\\n{'='*50}\")\n        print(f\"JACCARD INDEX (IoU): {jaccard:.4f}\")\n        print(f\"{'='*50}\")\n        print(f\"Recall: {cr['1']['recall']:.4f}\")\n        print(f\"Precision: {cr['1']['precision']:.4f}\")\n        print(f\"F1-Score: {cr['1']['f1-score']:.4f}\")\n        print(f\"Accuracy: {cr['accuracy']:.4f}\")\n        \n        # Check if recall meets minimum requirement\n        if cr['1']['recall'] >= 0.75:\n            print(f\"✓ Recall requirement MET (≥75%)\")\n        else:\n            print(f\"✗ Recall requirement NOT MET (<75%)\")\n\n        # Plot confusion matrix with Jaccard annotation\n        fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n        \n        # Confusion Matrix\n        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n                   xticklabels=['Not Target', 'Target'],\n                   yticklabels=['Not Target', 'Target'],\n                   ax=axes[0])\n        axes[0].set_title(f'Confusion Matrix\\nJaccard Index: {jaccard:.4f}')\n        axes[0].set_ylabel('Actual')\n        axes[0].set_xlabel('Predicted')\n        \n        # Add Jaccard calculation visualization\n        TP = cm[1, 1]\n        FP = cm[0, 1]\n        FN = cm[1, 0]\n        \n        axes[0].text(0.5, -0.15, \n                    f'Jaccard = TP/(TP+FP+FN) = {TP}/({TP}+{FP}+{FN}) = {jaccard:.4f}',\n                    transform=axes[0].transAxes,\n                    ha='center', fontsize=10, bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))\n        \n        # ROC-like visualization for probability distribution\n        axes[1].hist(y_pred_proba[y_test == 0], bins=50, alpha=0.5, label='Negative Class', color='blue')\n        axes[1].hist(y_pred_proba[y_test == 1], bins=50, alpha=0.5, label='Positive Class', color='red')\n        axes[1].set_xlabel('Prediction Probability')\n        axes[1].set_ylabel('Frequency')\n        axes[1].set_title('Prediction Probability Distribution')\n        axes[1].legend()\n        axes[1].grid(True, alpha=0.3)\n        \n        plt.tight_layout()\n        plt.show()\n\n        # Plot feature importance\n        feature_importance = self.model.get_feature_importance()\n        feature_names = self.feature_columns\n\n        importance_df = pd.DataFrame({\n            'feature': feature_names,\n            'importance': feature_importance\n        }).sort_values('importance', ascending=False).head(20)\n\n        plt.figure(figsize=(10, 8))\n        sns.barplot(data=importance_df, x='importance', y='feature')\n        plt.title('Top 20 Feature Importance')\n        plt.xlabel('Importance Score')\n        plt.tight_layout()\n        plt.show()\n\n        return {\n            'jaccard_index': jaccard,\n            'confusion_matrix': cm,\n            'classification_report': cr,\n            'predictions': y_pred,\n            'probabilities': y_pred_proba\n        }\n\n    def create_submission(self, eval_df, threshold=0.5):\n        \"\"\"Create submission CSV with predictions in required format\"\"\"\n        print(\"Creating submission file...\")\n\n        # Engineer features for evaluation set\n        eval_features = self.engineer_features(eval_df)\n\n        # Prepare features\n        X_eval = eval_features[self.feature_columns].copy()\n\n        # Handle missing values in evaluation set\n        for col in X_eval.columns:\n            if col in self.categorical_features:\n                # Convert categorical to string explicitly\n                X_eval[col] = X_eval[col].astype(str)\n                if X_eval[col].isnull().any():\n                    X_eval[col].fillna('missing', inplace=True)\n            else:\n                # Ensure numerical columns are float\n                X_eval[col] = pd.to_numeric(X_eval[col], errors='coerce')\n                if X_eval[col].isnull().any():\n                    X_eval[col].fillna(X_eval[col].median(), inplace=True)\n\n        # Get predictions\n        predictions_proba = self.model.predict_proba(X_eval)[:, 1]\n        predictions = (predictions_proba >= threshold).astype(int)\n\n        # Create submission dataframe with required format: mint_token_id, is_target\n        submission_df = pd.DataFrame({\n            'mint_token_id': eval_df['mint_token_id'],\n            'is_target': predictions\n        })\n\n        # Save submission file as exactly \"submission.csv\"\n        submission_filename = \"submission.csv\"\n        submission_df.to_csv(submission_filename, index=False)\n\n        print(f\"\\nSubmission file created: {submission_filename}\")\n        print(f\"Total rows in submission: {len(submission_df)}\")\n        print(f\"Tokens predicted as target (is_target=1): {submission_df['is_target'].sum()}\")\n        print(f\"Tokens predicted as non-target (is_target=0): {len(submission_df) - submission_df['is_target'].sum()}\")\n        print(f\"Target percentage: {submission_df['is_target'].mean()*100:.2f}%\")\n\n        # Verify the format\n        print(f\"\\nSubmission format verification:\")\n        print(f\"Columns: {list(submission_df.columns)}\")\n        print(f\"First few rows:\")\n        print(submission_df.head())\n\n        return submission_df\n\n    def run_pipeline(self):\n        \"\"\"Execute complete ML pipeline\"\"\"\n        print(\"\\n\" + \"=\"*60)\n        print(\"PUMP.FUN TOKEN PREDICTOR - ML PIPELINE\")\n        print(\"=\"*60 + \"\\n\")\n\n        # Load data\n        train_df, eval_df = self.load_data()\n\n        # Perform EDA on training data\n        self.perform_eda(train_df, \"Training Data EDA\")\n\n        # Engineer features\n        train_features = self.engineer_features(train_df)\n\n        # Create target variable\n        train_features = self.create_target_variable(train_features)\n\n        # Select features\n        self.select_features(train_features)\n\n        # Prepare model data\n        X, y = self.prepare_model_data(train_features)\n\n        # Split data with stratification\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        # Further split training data for validation\n        X_train_final, X_val, y_train_final, y_val = train_test_split(\n            X_train, y_train, test_size=0.2, random_state=42, stratify=y_train\n        )\n\n        print(f\"\\nData split:\")\n        print(f\"Training: {X_train_final.shape[0]} samples\")\n        print(f\"Validation: {X_val.shape[0]} samples\")\n        print(f\"Test: {X_test.shape[0]} samples\")\n\n        # Train model\n        self.train_model(X_train_final, y_train_final, X_val, y_val)\n\n        # Evaluate model\n        results = self.evaluate_model_with_jaccard(X_test, y_test)\n\n        # Create submission\n        submission_df = self.create_submission(eval_df)\n\n        print(\"\\n\" + \"=\"*60)\n        print(\"PIPELINE COMPLETED SUCCESSFULLY!\")\n        print(\"=\"*60)\n\n        return results, submission_df\n\n\n# Main execution\nif __name__ == \"__main__\":\n    # Define paths to your data\n    ROOT_PATH = \"/kaggle/input/alpha-radar-solana-sprint\"\n    SEPTEMBER_PATH = \"/kaggle/input/pumpfun-30s-september-2025\"\n\n    # Initialize predictor\n    predictor = PumpFunTokenPredictor(ROOT_PATH, SEPTEMBER_PATH)\n\n    # Run complete pipeline\n    results, submission_df = predictor.run_pipeline()\n\n    # Display final metrics\n    print(\"\\n\" + \"=\"*60)\n    print(\"FINAL METRICS SUMMARY\")\n    print(\"=\"*60)\n    print(f\"Jaccard Index: {results['jaccard_index']:.4f}\")\n    print(f\"Recall: {results['classification_report']['1']['recall']:.4f}\")\n    print(f\"Precision: {results['classification_report']['1']['precision']:.4f}\")\n    print(f\"F1-Score: {results['classification_report']['1']['f1-score']:.4f}\")\n    print(\"=\"*60)","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}