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+"""
+Advanced Project: Machine Learning Data Pipeline
+================================================
+
+This project demonstrates building a production-ready ML data pipeline.
+It covers advanced Python concepts including:
+- Generator functions and iterators
+- Context managers
+- Decorators for timing and logging
+- Type hints and data validation
+- Async/await patterns
+- Data transformation pipelines
+
+Features:
+- Lazy data loading with generators
+- Pipeline pattern for data transformations
+- Performance monitoring
+- Error handling and recovery
+- Batch processing
+
+Requirements:
+    pip install pandas numpy scikit-learn (optional, for full ML features)
+"""
+
+import time
+import logging
+from datetime import datetime
+from typing import List, Dict, Any, Callable, Iterator, Optional, TypeVar, Generic
+from functools import wraps
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+import json
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+# Type variables for generic pipeline
+T = TypeVar('T')
+R = TypeVar('R')
+
+
+def timing_decorator(func: Callable) -> Callable:
+    """Decorator to measure function execution time"""
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        start_time = time.perf_counter()
+        result = func(*args, **kwargs)
+        end_time = time.perf_counter()
+        elapsed = end_time - start_time
+        logger.info(f"{func.__name__} executed in {elapsed:.4f} seconds")
+        return result
+    return wrapper
+
+
+@contextmanager
+def pipeline_stage(stage_name: str):
+    """Context manager for pipeline stage monitoring"""
+    logger.info(f"🚀 Starting stage: {stage_name}")
+    start_time = time.time()
+    try:
+        yield
+        elapsed = time.time() - start_time
+        logger.info(f"✅ Completed stage: {stage_name} ({elapsed:.2f}s)")
+    except Exception as e:
+        elapsed = time.time() - start_time
+        logger.error(f"❌ Failed stage: {stage_name} after {elapsed:.2f}s - {str(e)}")
+        raise
+
+
+@dataclass
+class DataRecord:
+    """Represents a single data record"""
+    id: int
+    features: Dict[str, float]
+    label: Optional[int] = None
+    timestamp: datetime = field(default_factory=datetime.now)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            'id': self.id,
+            'features': self.features,
+            'label': self.label,
+            'timestamp': self.timestamp.isoformat()
+        }
+
+
+class DataGenerator:
+    """
+    Generator class for lazy data loading
+    
+    Demonstrates:
+    - Generator functions
+    - Memory-efficient data loading
+    - Iterator protocol
+    """
+    
+    def __init__(self, n_samples: int = 1000):
+        self.n_samples = n_samples
+    
+    def generate_sample_data(self) -> Iterator[DataRecord]:
+        """Generate sample data records lazily"""
+        for i in range(self.n_samples):
+            record = DataRecord(
+                id=i,
+                features={
+                    'feature_1': float(i % 10),
+                    'feature_2': float(i % 7),
+                    'feature_3': float(i % 5)
+                },
+                label=i % 3
+            )
+            yield record
+    
+    def batch_generator(self, batch_size: int = 32) -> Iterator[List[DataRecord]]:
+        """Generate batches of data"""
+        batch = []
+        for record in self.generate_sample_data():
+            batch.append(record)
+            if len(batch) >= batch_size:
+                yield batch
+                batch = []
+        if batch:
+            yield batch
+
+
+class PipelineStep(Generic[T, R]):
+    """Generic pipeline step with transform functionality"""
+    
+    def __init__(self, name: str, transform_func: Callable[[T], R]):
+        self.name = name
+        self.transform_func = transform_func
+    
+    @timing_decorator
+    def execute(self, data: T) -> R:
+        """Execute the transformation"""
+        return self.transform_func(data)
+
+
+class DataPipeline:
+    """
+    Data Processing Pipeline
+    
+    Demonstrates:
+    - Chain of responsibility pattern
+    - Generic type hints
+    - Pipeline composition
+    """
+    
+    def __init__(self, name: str = "ML Pipeline"):
+        self.name = name
+        self.steps: List[PipelineStep] = []
+    
+    def add_step(self, name: str, transform_func: Callable) -> 'DataPipeline':
+        """Add a step to the pipeline"""
+        step = PipelineStep(name, transform_func)
+        self.steps.append(step)
+        return self
+    
+    @timing_decorator
+    def run(self, data: Any) -> Any:
+        """Execute all pipeline steps sequentially"""
+        logger.info(f"Running pipeline: {self.name}")
+        result = data
+        
+        for step in self.steps:
+            with pipeline_stage(step.name):
+                result = step.execute(result)
+        
+        logger.info(f"Pipeline {self.name} completed successfully")
+        return result
+
+
+def create_sample_pipeline() -> DataPipeline:
+    """Create a sample data processing pipeline"""
+    
+    def normalize_features(data: List[DataRecord]) -> List[DataRecord]:
+        """Normalize feature values"""
+        for record in data:
+            max_val = max(record.features.values()) or 1
+            record.features = {k: v/max_val for k, v in record.features.items()}
+        return data
+    
+    def filter_outliers(data: List[DataRecord]) -> List[DataRecord]:
+        """Filter out outlier records"""
+        return [r for r in data if sum(r.features.values()) < 20]
+    
+    def add_metadata(data: List[DataRecord]) -> List[DataRecord]:
+        """Add metadata to records"""
+        for record in data:
+            record.features['record_count'] = len(data)
+        return data
+    
+    pipeline = DataPipeline("Sample ML Pipeline")
+    pipeline.add_step("Normalize Features", normalize_features)
+    pipeline.add_step("Filter Outliers", filter_outliers)
+    pipeline.add_step("Add Metadata", add_metadata)
+    
+    return pipeline
+
+
+def main():
+    """Main entry point demonstrating the ML pipeline"""
+    print("=" * 60)
+    print("Advanced Project: ML Data Pipeline")
+    print("=" * 60)
+    
+    # Create data generator
+    generator = DataGenerator(n_samples=100)
+    
+    # Load first batch of data
+    print("\n📊 Loading sample data...")
+    data_batch = next(generator.batch_generator(batch_size=10))
+    print(f"   Loaded {len(data_batch)} records")
+    
+    # Display sample record
+    print("\n📋 Sample Record:")
+    print(f"   {json.dumps(data_batch[0].to_dict(), indent=2)}")
+    
+    # Create and run pipeline
+    print("\n⚙️  Creating data processing pipeline...")
+    pipeline = create_sample_pipeline()
+    
+    # Execute pipeline
+    print("\n🔄 Running pipeline...")
+    processed_data = pipeline.run(data_batch)
+    
+    # Display results
+    print(f"\n✅ Processed {len(processed_data)} records")
+    print("\n📋 Processed Sample Record:")
+    print(f"   {json.dumps(processed_data[0].to_dict(), indent=2)}")
+    
+    # Demonstrate async-style processing
+    print("\n⚡ Demonstrating batch processing...")
+    total_processed = 0
+    for batch in generator.batch_generator(batch_size=20):
+        processed = pipeline.run(batch)
+        total_processed += len(processed)
+    
+    print(f"\n✨ Total records processed: {total_processed}")
+    
+    print("\n" + "=" * 60)
+    print("ML Pipeline demonstration complete!")
+    print("=" * 60)
+    print("\n💡 To extend this pipeline:")
+    print("   - Add database connectors")
+    print("   - Integrate with scikit-learn models")
+    print("   - Add distributed processing with Dask/Spark")
+    print()
+
+
+if __name__ == '__main__':
+    main()