class ProductionKNNImputer:
    """
    Production-ready KNN imputation with monitoring and error handling
    """

    def __init__(self, config_path=None):
        self.config = self._load_config(config_path)
        self.imputer = None
        self.scaler = None
        self.encoders = {}
        self.feature_names = None
        self.is_fitted = False

    def _load_config(self, config_path):
        """Load configuration from file or use defaults"""
        default_config = {
            'n_neighbors': 5,
            'weights': 'distance',
            'algorithm': 'auto',
            'chunk_size': 10000,
            'n_jobs': -1,
            'validation_split': 0.2,
            'max_memory_gb': 8
        }

        if config_path:
            import json
            with open(config_path, 'r') as f:
                user_config = json.load(f)
            default_config.update(user_config)

        return default_config

    def _estimate_memory_usage(self, n_samples, n_features):
        """Estimate memory usage for KNN imputation"""
        # Rough estimate: distance matrix + feature matrix
        distance_matrix_gb = (n_samples ** 2 * 8) / (1024 ** 3)
        feature_matrix_gb = (n_samples * n_features * 8) / (1024 ** 3)
        total_gb = distance_matrix_gb + feature_matrix_gb

        return total_gb

    def _preprocess_data(self, df