AI-Powered Analytics: The Future of Data - Revolutionary Intelligence Transforming Business Decision-Making Through Automated Insights, Predictive Analytics, and Real-Time Intelligence

import asyncio
import json
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Callable, Union
from dataclasses import dataclass, field
from enum import Enum
import uuid
import time
from concurrent.futures import ThreadPoolExecutor
from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error, classification_report

class DataType(Enum):
    STRUCTURED = "structured_data"
    UNSTRUCTURED = "unstructured_data"
    TIME_SERIES = "time_series"
    STREAMING = "streaming_data"
    IMAGE = "image_data"
    TEXT = "text_data"
    MULTIMODAL = "multimodal_data"

class AnalyticsType(Enum):
    DESCRIPTIVE = "descriptive_analytics"
    DIAGNOSTIC = "diagnostic_analytics"
    PREDICTIVE = "predictive_analytics"
    PRESCRIPTIVE = "prescriptive_analytics"
    COGNITIVE = "cognitive_analytics"

class ModelType(Enum):
    CLASSIFICATION = "classification"
    REGRESSION = "regression"
    CLUSTERING = "clustering"
    ANOMALY_DETECTION = "anomaly_detection"
    RECOMMENDATION = "recommendation"
    NLP = "natural_language_processing"
    COMPUTER_VISION = "computer_vision"

@dataclass
class DataSource:
    """Represents a data source in the analytics platform"""
    id: str
    name: str
    data_type: DataType
    connection_string: str
    schema_definition: Dict[str, Any]
    update_frequency: str
    data_quality_score: float = 0.0
    last_updated: datetime = field(default_factory=datetime.now)
    metadata: Dict[str, Any] = field(default_factory=dict)
    
@dataclass
class AnalyticsModel:
    """Represents an AI/ML model in the analytics platform"""
    id: str
    name: str
    model_type: ModelType
    algorithm: str
    training_data_sources: List[str]
    performance_metrics: Dict[str, float] = field(default_factory=dict)
    last_trained: datetime = field(default_factory=datetime.now)
    model_version: str = "1.0"
    deployment_status: str = "active"
    prediction_confidence: float = 0.0
    
@dataclass
class AnalyticsInsight:
    """Represents an analytical insight generated by the platform"""
    id: str
    insight_type: AnalyticsType
    title: str
    description: str
    confidence_score: float
    business_impact: str
    recommended_actions: List[str]
    supporting_data: Dict[str, Any]
    generated_timestamp: datetime = field(default_factory=datetime.now)
    
class AIAnalyticsPlatform:
    """Comprehensive AI-powered analytics platform"""
    
    def __init__(self, platform_name: str):
        self.platform_name = platform_name
        self.data_sources: Dict[str, DataSource] = {}
        self.models: Dict[str, AnalyticsModel] = {}
        self.insights: List[AnalyticsInsight] = []
        self.processed_data: Dict[str, pd.DataFrame] = {}
        
        # AI and ML components
        self.ml_engine = MLEngine()
        self.nlp_processor = NLPProcessor()
        self.computer_vision = ComputerVisionEngine()
        self.predictive_engine = PredictiveEngine()
        
        # Data processing and quality
        self.data_processor = DataProcessor()
        self.quality_monitor = DataQualityMonitor()
        
        # Real-time analytics
        self.streaming_engine = StreamingAnalyticsEngine()
        
        # Conversational analytics
        self.conversational_ai = ConversationalAnalytics()
        
        # Automated insights
        self.insight_generator = AutomatedInsightGenerator()
        
        print(f"AI Analytics Platform '{platform_name}' initialized")
        
    def connect_data_source(self, data_source: DataSource) -> Dict[str, Any]:
        """Connect a new data source to the analytics platform"""
        print(f"Connecting data source: {data_source.name}")
        
        # Add data source to platform
        self.data_sources[data_source.id] = data_source
        
        # Test connection and validate schema
        connection_test = self._test_data_connection(data_source)
        
        # Assess data quality
        quality_assessment = self.quality_monitor.assess_data_quality(data_source)
        
        # Set up automated data ingestion
        ingestion_config = self._configure_data_ingestion(data_source)
        
        # Initialize data profiling
        data_profile = self._profile_data_source(data_source)
        
        connection_result = {
            "data_source_id": data_source.id,
            "connection_timestamp": datetime.now(),
            "connection_status": connection_test["status"],
            "quality_assessment": quality_assessment,
            "ingestion_config": ingestion_config,
            "data_profile": data_profile,
            "estimated_processing_time": self._estimate_processing_time(data_source),
            "recommended_models": self._recommend_models_for_data(data_source)
        }
        
        print(f"Data source {data_source.name} connected successfully")
        return connection_result
        
    def _test_data_connection(self, data_source: DataSource) -> Dict[str, Any]:
        """Test connection to data source and validate accessibility"""
        # Simulate connection testing
        return {
            "status": "successful",
            "response_time_ms": np.random.randint(50, 200),
            "data_availability": True,
            "schema_validation": "passed",
            "access_permissions": "read_write"
        }
        
    def _profile_data_source(self, data_source: DataSource) -> Dict[str, Any]:
        """Generate comprehensive profile of data source characteristics"""
        # Simulate data profiling
        profile = {
            "row_count": np.random.randint(10000, 1000000),
            "column_count": np.random.randint(10, 100),
            "data_types": {
                "numeric": np.random.randint(5, 30),
                "categorical": np.random.randint(3, 20),
                "datetime": np.random.randint(1, 5),
                "text": np.random.randint(2, 15)
            },
            "null_percentage": np.random.uniform(0.01, 0.15),
            "duplicate_percentage": np.random.uniform(0.001, 0.05),
            "data_freshness": "real_time" if data_source.update_frequency == "streaming" else "batch",
            "complexity_score": np.random.uniform(0.3, 0.9)
        }
        
        return profile
        
    async def process_data_for_analysis(self, data_source_id: str, 
                                      analysis_requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Process data from source for specific analytical requirements"""
        if data_source_id not in self.data_sources:
            return {"error": "Data source not found"}
            
        data_source = self.data_sources[data_source_id]
        
        print(f"Processing data from {data_source.name} for analysis")
        
        # Extract and load data
        raw_data = await self._extract_data(data_source)
        
        # Clean and preprocess data
        cleaned_data = self.data_processor.clean_data(raw_data, analysis_requirements)
        
        # Feature engineering
        engineered_features = self.data_processor.engineer_features(
            cleaned_data, analysis_requirements
        )
        
        # Data transformation and normalization
        transformed_data = self.data_processor.transform_data(
            engineered_features, analysis_requirements
        )
        
        # Store processed data
        self.processed_data[data_source_id] = transformed_data
        
        # Generate data processing report
        processing_report = {
            "data_source_id": data_source_id,
            "processing_timestamp": datetime.now(),
            "raw_data_shape": raw_data.shape if hasattr(raw_data, 'shape') else "unknown",
            "processed_data_shape": transformed_data.shape if hasattr(transformed_data, 'shape') else "unknown",
            "data_quality_improvement": self._calculate_quality_improvement(raw_data, transformed_data),
            "feature_count": len(engineered_features.columns) if hasattr(engineered_features, 'columns') else 0,
            "processing_time_seconds": np.random.uniform(5, 30),
            "data_readiness_score": np.random.uniform(0.85, 0.98)
        }
        
        return processing_report
        
    async def _extract_data(self, data_source: DataSource) -> Any:
        """Extract data from source based on data type"""
        # Simulate data extraction based on data type
        if data_source.data_type == DataType.STRUCTURED:
            # Generate sample structured data
            return pd.DataFrame({
                'feature_1': np.random.normal(100, 20, 10000),
                'feature_2': np.random.uniform(0, 1, 10000),
                'feature_3': np.random.choice(['A', 'B', 'C'], 10000),
                'target': np.random.randint(0, 2, 10000)
            })
        elif data_source.data_type == DataType.TIME_SERIES:
            # Generate sample time series data
            dates = pd.date_range(start='2024-01-01', end='2025-08-31', freq='D')
            return pd.DataFrame({
                'timestamp': dates,
                'value': np.cumsum(np.random.normal(0, 1, len(dates))) + 100,
                'category': np.random.choice(['X', 'Y', 'Z'], len(dates))
            })
        else:
            # Return simulated data for other types
            return pd.DataFrame(np.random.rand(1000, 10))
            
    def train_predictive_model(self, model_config: Dict[str, Any]) -> Dict[str, Any]:
        """Train AI/ML model for predictive analytics"""
        model_name = model_config.get("name", f"model_{uuid.uuid4()}")
        model_type = ModelType(model_config.get("type", "classification"))
        data_source_ids = model_config.get("data_sources", [])
        
        print(f"Training predictive model: {model_name}")
        
        # Prepare training data
        training_data = self._prepare_training_data(data_source_ids, model_config)
        
        # Select optimal algorithm
        algorithm = self._select_optimal_algorithm(model_type, training_data)
        
        # Train model
        trained_model = self.ml_engine.train_model(
            algorithm, training_data, model_config
        )
        
        # Evaluate model performance
        performance_metrics = self._evaluate_model_performance(
            trained_model, training_data, model_type
        )
        
        # Create model object
        analytics_model = AnalyticsModel(
            id=f"model_{uuid.uuid4()}",
            name=model_name,
            model_type=model_type,
            algorithm=algorithm["name"],
            training_data_sources=data_source_ids,
            performance_metrics=performance_metrics,
            prediction_confidence=performance_metrics.get("confidence", 0.0)
        )
        
        self.models[analytics_model.id] = analytics_model
        
        training_result = {
            "model_id": analytics_model.id,
            "training_timestamp": datetime.now(),
            "algorithm_selected": algorithm["name"],
            "performance_metrics": performance_metrics,
            "training_data_size": len(training_data),
            "model_complexity": algorithm["complexity"],
            "deployment_ready": performance_metrics.get("accuracy", 0) > 0.8,
            "recommended_use_cases": self._generate_use_case_recommendations(analytics_model)
        }
        
        print(f"Model {model_name} trained successfully with {performance_metrics.get('accuracy', 0):.2%} accuracy")
        return training_result
        
    def _prepare_training_data(self, data_source_ids: List[str], 
                             model_config: Dict[str, Any]) -> pd.DataFrame:
        """Prepare and combine data from multiple sources for model training"""
        combined_data = pd.DataFrame()
        
        for source_id in data_source_ids:
            if source_id in self.processed_data:
                source_data = self.processed_data[source_id]
                combined_data = pd.concat([combined_data, source_data], ignore_index=True)
                
        # Handle missing target variable
        target_column = model_config.get("target_column", "target")
        if target_column not in combined_data.columns:
            # Generate synthetic target for demonstration
            combined_data[target_column] = np.random.randint(0, 2, len(combined_data))
            
        return combined_data
        
    def _select_optimal_algorithm(self, model_type: ModelType, 
                                training_data: pd.DataFrame) -> Dict[str, Any]:
        """Select optimal algorithm based on model type and data characteristics"""
        data_size = len(training_data)
        feature_count = len(training_data.columns) - 1  # Exclude target
        
        if model_type == ModelType.CLASSIFICATION:
            if data_size > 100000 and feature_count > 50:
                return {"name": "GradientBoostingClassifier", "complexity": "high"}
            elif data_size > 10000:
                return {"name": "RandomForestClassifier", "complexity": "medium"}
            else:
                return {"name": "LogisticRegression", "complexity": "low"}
        elif model_type == ModelType.REGRESSION:
            if data_size > 50000:
                return {"name": "GradientBoostingRegressor", "complexity": "high"}
            else:
                return {"name": "RandomForestRegressor", "complexity": "medium"}
        else:
            return {"name": "AutoML", "complexity": "adaptive"}
            
    def generate_automated_insights(self, analysis_scope: str = "comprehensive") -> List[AnalyticsInsight]:
        """Generate automated insights across all connected data sources and models"""
        print(f"Generating automated insights with {analysis_scope} scope")
        
        insights = []
        
        # Performance insights from models
        model_insights = self._generate_model_performance_insights()
        insights.extend(model_insights)
        
        # Data quality insights
        quality_insights = self._generate_data_quality_insights()
        insights.extend(quality_insights)
        
        # Trend and pattern insights
        pattern_insights = self._generate_pattern_insights()
        insights.extend(pattern_insights)
        
        # Business impact insights
        business_insights = self._generate_business_impact_insights()
        insights.extend(business_insights)
        
        # Predictive insights
        predictive_insights = self._generate_predictive_insights()
        insights.extend(predictive_insights)
        
        # Store insights
        self.insights.extend(insights)
        
        # Rank insights by business impact and confidence
        ranked_insights = sorted(insights, 
                               key=lambda x: (x.confidence_score, 
                                             len(x.recommended_actions)), 
                               reverse=True)
        
        return ranked_insights[:10]  # Return top 10 insights
        
    def _generate_model_performance_insights(self) -> List[AnalyticsInsight]:
        """Generate insights about model performance and recommendations"""
        insights = []
        
        for model_id, model in self.models.items():
            accuracy = model.performance_metrics.get("accuracy", 0.0)
            
            if accuracy > 0.9:
                insight = AnalyticsInsight(
                    id=f"insight_{uuid.uuid4()}",
                    insight_type=AnalyticsType.DESCRIPTIVE,
                    title=f"High Performance Model: {model.name}",
                    description=f"Model {model.name} achieved {accuracy:.1%} accuracy, indicating excellent predictive capability",
                    confidence_score=0.95,
                    business_impact="high",
                    recommended_actions=[
                        "Deploy model to production for real-time predictions",
                        "Expand model usage to additional use cases",
                        "Monitor model performance for any degradation"
                    ],
                    supporting_data={"model_id": model_id, "accuracy": accuracy}
                )
                insights.append(insight)
            elif accuracy < 0.7:
                insight = AnalyticsInsight(
                    id=f"insight_{uuid.uuid4()}",
                    insight_type=AnalyticsType.DIAGNOSTIC,
                    title=f"Model Performance Issue: {model.name}",
                    description=f"Model {model.name} shows low accuracy of {accuracy:.1%}, requiring attention",
                    confidence_score=0.88,
                    business_impact="medium",
                    recommended_actions=[
                        "Collect additional training data",
                        "Perform feature engineering optimization",
                        "Consider alternative algorithms",
                        "Review data quality and preprocessing steps"
                    ],
                    supporting_data={"model_id": model_id, "accuracy": accuracy}
                )
                insights.append(insight)
                
        return insights
        
    def _generate_pattern_insights(self) -> List[AnalyticsInsight]:
        """Generate insights about data patterns and trends"""
        insights = []
        
        # Simulate pattern detection
        patterns = [
            {
                "pattern": "Seasonal trend detected in sales data",
                "confidence": 0.87,
                "impact": "Revenue planning and inventory management",
                "actions": [
                    "Adjust inventory levels based on seasonal patterns",
                    "Plan marketing campaigns around peak seasons",
                    "Optimize staffing for seasonal demand"
                ]
            },
            {
                "pattern": "Customer churn correlation with support interactions",
                "confidence": 0.79,
                "impact": "Customer retention and satisfaction",
                "actions": [
                    "Improve customer support processes",
                    "Implement proactive customer outreach",
                    "Monitor support interaction quality metrics"
                ]
            },
            {
                "pattern": "Geographic clustering in product preferences",
                "confidence": 0.83,
                "impact": "Marketing and product development",
                "actions": [
                    "Develop region-specific marketing strategies",
                    "Customize product offerings by geography",
                    "Optimize distribution and logistics"
                ]
            }
        ]
        
        for pattern_data in patterns:
            insight = AnalyticsInsight(
                id=f"insight_{uuid.uuid4()}",
                insight_type=AnalyticsType.PREDICTIVE,
                title=f"Pattern Discovery: {pattern_data['pattern']}",
                description=f"Advanced pattern analysis revealed: {pattern_data['pattern']} with {pattern_data['confidence']:.1%} confidence",
                confidence_score=pattern_data["confidence"],
                business_impact=pattern_data["impact"],
                recommended_actions=pattern_data["actions"],
                supporting_data={"pattern_type": "correlation_analysis"}
            )
            insights.append(insight)
            
        return insights
        
    async def process_natural_language_query(self, query: str, 
                                           context: Dict[str, Any] = None) -> Dict[str, Any]:
        """Process natural language query and return analytical insights"""
        print(f"Processing natural language query: '{query}'")
        
        # Parse query intent and extract analytical requirements
        query_analysis = self.nlp_processor.analyze_query(query)
        
        # Identify relevant data sources and models
        relevant_sources = self._identify_relevant_sources(query_analysis)
        
        # Generate analytical response
        analytical_response = await self._generate_analytical_response(
            query_analysis, relevant_sources, context
        )
        
        # Create visualization recommendations
        viz_recommendations = self._recommend_visualizations(analytical_response)
        
        # Generate natural language explanation
        explanation = self.nlp_processor.generate_explanation(
            query, analytical_response, viz_recommendations
        )
        
        query_result = {
            "query_id": f"query_{uuid.uuid4()}",
            "original_query": query,
            "query_timestamp": datetime.now(),
            "query_analysis": query_analysis,
            "relevant_data_sources": relevant_sources,
            "analytical_response": analytical_response,
            "visualization_recommendations": viz_recommendations,
            "natural_language_explanation": explanation,
            "confidence_score": analytical_response.get("confidence", 0.0),
            "execution_time_seconds": np.random.uniform(2, 8)
        }
        
        return query_result
        
    def _identify_relevant_sources(self, query_analysis: Dict[str, Any]) -> List[str]:
        """Identify data sources relevant to the query"""
        # Simulate source identification based on query analysis
        query_keywords = query_analysis.get("keywords", [])
        query_intent = query_analysis.get("intent", "")
        
        relevant_sources = []
        
        for source_id, source in self.data_sources.items():
            # Check if source metadata matches query keywords
            source_relevance = self._calculate_source_relevance(
                source, query_keywords, query_intent
            )
            
            if source_relevance > 0.5:
                relevant_sources.append(source_id)
                
        return relevant_sources[:5]  # Return top 5 most relevant sources
        
    async def _generate_analytical_response(self, query_analysis: Dict[str, Any], 
                                          relevant_sources: List[str], 
                                          context: Dict[str, Any]) -> Dict[str, Any]:
        """Generate analytical response based on query and available data"""
        query_type = query_analysis.get("analytics_type", "descriptive")
        
        if query_type == "predictive":
            # Use predictive models
            response = await self._generate_predictive_response(query_analysis, relevant_sources)
        elif query_type == "comparative":
            # Generate comparative analysis
            response = await self._generate_comparative_response(query_analysis, relevant_sources)
        elif query_type == "trend":
            # Analyze trends
            response = await self._generate_trend_response(query_analysis, relevant_sources)
        else:
            # Default descriptive analysis
            response = await self._generate_descriptive_response(query_analysis, relevant_sources)
            
        response["confidence"] = np.random.uniform(0.75, 0.95)
        response["data_coverage"] = len(relevant_sources) / max(len(self.data_sources), 1)
        
        return response
        
    def generate_comprehensive_report(self) -> Dict[str, Any]:
        """Generate comprehensive analytics platform performance report"""
        report = {
            "platform_name": self.platform_name,
            "report_timestamp": datetime.now(),
            "data_ecosystem_overview": self._analyze_data_ecosystem(),
            "model_performance_summary": self._summarize_model_performance(),
            "insights_generation_metrics": self._calculate_insights_metrics(),
            "platform_utilization": self._calculate_platform_utilization(),
            "roi_analysis": self._calculate_roi_metrics(),
            "data_quality_assessment": self._assess_overall_data_quality(),
            "predictive_accuracy_trends": self._analyze_accuracy_trends(),
            "user_engagement_metrics": self._calculate_user_engagement(),
            "optimization_recommendations": self._generate_optimization_recommendations()
        }
        
        return report
        
    # Helper methods for analytics and calculations
    def _analyze_data_ecosystem(self) -> Dict[str, Any]:
        """Analyze the overall data ecosystem health and coverage"""
        total_sources = len(self.data_sources)
        active_sources = len([s for s in self.data_sources.values() 
                            if s.data_quality_score > 0.7])
        
        data_types = {}
        for data_type in DataType:
            count = len([s for s in self.data_sources.values() 
                        if s.data_type == data_type])
            data_types[data_type.value] = count
            
        return {
            "total_data_sources": total_sources,
            "active_sources": active_sources,
            "data_source_health": (active_sources / total_sources * 100) if total_sources > 0 else 0,
            "data_type_distribution": data_types,
            "total_processed_records": sum([len(df) for df in self.processed_data.values()]),
            "average_data_quality": np.mean([s.data_quality_score for s in self.data_sources.values()]) if self.data_sources else 0
        }
        
    def _summarize_model_performance(self) -> Dict[str, Any]:
        """Summarize performance across all models"""
        if not self.models:
            return {"status": "No models deployed"}
            
        total_models = len(self.models)
        high_performance_models = len([
            m for m in self.models.values() 
            if m.performance_metrics.get("accuracy", 0) > 0.85
        ])
        
        avg_accuracy = np.mean([
            m.performance_metrics.get("accuracy", 0) 
            for m in self.models.values()
        ])
        
        model_types = {}
        for model_type in ModelType:
            count = len([m for m in self.models.values() if m.model_type == model_type])
            if count > 0:
                model_types[model_type.value] = count
                
        return {
            "total_models": total_models,
            "high_performance_models": high_performance_models,
            "average_accuracy": avg_accuracy,
            "model_type_distribution": model_types,
            "deployment_success_rate": (high_performance_models / total_models * 100) if total_models > 0 else 0,
            "average_confidence": np.mean([m.prediction_confidence for m in self.models.values()]) if self.models else 0
        }
        
    def _generate_optimization_recommendations(self) -> List[Dict[str, Any]]:
        """Generate recommendations for platform optimization"""
        recommendations = []
        
        # Data quality recommendations
        low_quality_sources = [
            s for s in self.data_sources.values() 
            if s.data_quality_score < 0.7
        ]
        
        if low_quality_sources:
            recommendations.append({
                "category": "Data Quality",
                "recommendation": "Improve data quality for underperforming sources",
                "priority": "high",
                "impact": "Improved model accuracy and reliability",
                "affected_sources": len(low_quality_sources)
            })
            
        # Model performance recommendations
        low_performance_models = [
            m for m in self.models.values() 
            if m.performance_metrics.get("accuracy", 0) < 0.75
        ]
        
        if low_performance_models:
            recommendations.append({
                "category": "Model Performance",
                "recommendation": "Retrain or optimize underperforming models",
                "priority": "medium",
                "impact": "Enhanced predictive accuracy and business value",
                "affected_models": len(low_performance_models)
            })
            
        # Capacity and scaling recommendations
        if len(self.processed_data) > 10:
            recommendations.append({
                "category": "Platform Scaling",
                "recommendation": "Consider implementing distributed processing for large datasets",
                "priority": "medium",
                "impact": "Improved processing speed and system performance",
                "estimated_improvement": "50% faster processing"
            })
            
        return recommendations

# Specialized AI and analytics components
class MLEngine:
    """Machine learning engine for model training and optimization"""
    
    def train_model(self, algorithm: Dict[str, Any], training_data: pd.DataFrame, 
                   config: Dict[str, Any]) -> Any:
        """Train machine learning model with specified algorithm"""
        algorithm_name = algorithm["name"]
        target_column = config.get("target_column", "target")
        
        # Prepare features and target
        X = training_data.drop(columns=[target_column])
        y = training_data[target_column]
        
        # Handle categorical variables
        categorical_columns = X.select_dtypes(include=['object']).columns
        for col in categorical_columns:
            le = LabelEncoder()
            X[col] = le.fit_transform(X[col].astype(str))
            
        # Split data
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        # Select and train model
        if algorithm_name == "RandomForestClassifier":
            model = RandomForestClassifier(n_estimators=100, random_state=42)
        elif algorithm_name == "GradientBoostingRegressor":
            model = GradientBoostingRegressor(n_estimators=100, random_state=42)
        else:
            # Default to RandomForest
            model = RandomForestClassifier(n_estimators=100, random_state=42)
            
        # Train model
        model.fit(X_train, y_train)
        
        # Store test data for evaluation
        model.X_test = X_test
        model.y_test = y_test
        
        return model
        
class NLPProcessor:
    """Natural language processing for conversational analytics"""
    
    def analyze_query(self, query: str) -> Dict[str, Any]:
        """Analyze natural language query to extract intent and requirements"""
        # Simulate NLP analysis
        query_lower = query.lower()
        
        # Determine analytics type
        if any(word in query_lower for word in ['predict', 'forecast', 'future']):
            analytics_type = "predictive"
        elif any(word in query_lower for word in ['compare', 'vs', 'versus', 'difference']):
            analytics_type = "comparative"
        elif any(word in query_lower for word in ['trend', 'over time', 'change']):
            analytics_type = "trend"
        else:
            analytics_type = "descriptive"
            
        # Extract keywords
        keywords = [word for word in query_lower.split() 
                   if len(word) > 3 and word not in ['what', 'how', 'when', 'where', 'why']]
        
        return {
            "intent": "analytical_query",
            "analytics_type": analytics_type,
            "keywords": keywords[:10],  # Top 10 keywords
            "complexity": "medium",
            "requires_visualization": True,
            "confidence": 0.85
        }
        
    def generate_explanation(self, query: str, response: Dict[str, Any], 
                           visualizations: List[str]) -> str:
        """Generate natural language explanation of analytical results"""
        confidence = response.get("confidence", 0.8)
        
        explanation = f"Based on your query '{query}', I analyzed the available data and found the following insights: "
        explanation += f"The analysis shows a confidence level of {confidence:.1%}. "
        
        if visualizations:
            explanation += f"I recommend visualizing these results using {', '.join(visualizations[:3])}. "
            
        explanation += "These insights can help inform your decision-making process and strategic planning."
        
        return explanation

# Additional specialized components would continue here...

def create_sample_analytics_platform():
    """Create sample AI analytics platform with data sources and models"""
    platform = AIAnalyticsPlatform("Enterprise AI Analytics")
    
    # Create sample data sources
    sales_data = DataSource(
        id="sales_001",
        name="Sales Transaction Data",
        data_type=DataType.STRUCTURED,
        connection_string="postgresql://sales_db",
        schema_definition={
            "transaction_id": "string",
            "amount": "float",
            "customer_id": "string",
            "product_category": "string",
            "timestamp": "datetime"
        },
        update_frequency="real_time",
        data_quality_score=0.92
    )
    
    customer_data = DataSource(
        id="customer_001",
        name="Customer Behavior Data",
        data_type=DataType.STRUCTURED,
        connection_string="mysql://customer_db",
        schema_definition={
            "customer_id": "string",
            "age": "integer",
            "location": "string",
            "purchase_history": "json"
        },
        update_frequency="daily",
        data_quality_score=0.87
    )
    
    time_series_data = DataSource(
        id="metrics_001",
        name="Performance Metrics Time Series",
        data_type=DataType.TIME_SERIES,
        connection_string="influxdb://metrics",
        schema_definition={
            "timestamp": "datetime",
            "metric_name": "string",
            "value": "float",
            "tags": "json"
        },
        update_frequency="streaming",
        data_quality_score=0.95
    )
    
    return platform, [sales_data, customer_data, time_series_data]

async def run_ai_analytics_demo():
    print("=== AI-Powered Analytics Platform Demo ===")
    
    # Create analytics platform
    platform, data_sources = create_sample_analytics_platform()
    print(f"Created AI analytics platform with {len(data_sources)} data sources")
    
    # Connect data sources
    print("\n--- Connecting Data Sources ---")
    for data_source in data_sources:
        connection_result = platform.connect_data_source(data_source)
        print(f"Connected {data_source.name}: {connection_result['connection_status']}")
        print(f"Data quality: {connection_result['quality_assessment'].get('overall_score', 0.85):.1%}")
        
    # Process data for analysis
    print("\n--- Processing Data for Analysis ---")
    for data_source in data_sources:
        analysis_requirements = {
            "target_column": "target",
            "feature_selection": "automatic",
            "preprocessing": "standard"
        }
        
        processing_result = await platform.process_data_for_analysis(
            data_source.id, analysis_requirements
        )
        print(f"Processed {data_source.name}: {processing_result['data_readiness_score']:.1%} readiness")
        
    # Train predictive models
    print("\n--- Training Predictive Models ---")
    model_configs = [
        {
            "name": "Sales Prediction Model",
            "type": "regression",
            "data_sources": ["sales_001"],
            "target_column": "amount"
        },
        {
            "name": "Customer Segmentation Model",
            "type": "classification",
            "data_sources": ["customer_001"],
            "target_column": "segment"
        }
    ]
    
    for config in model_configs:
        training_result = platform.train_predictive_model(config)
        print(f"Trained {config['name']}: {training_result['performance_metrics'].get('accuracy', 0.85):.1%} accuracy")
        
    # Generate automated insights
    print("\n--- Generating Automated Insights ---")
    insights = platform.generate_automated_insights()
    print(f"Generated {len(insights)} automated insights")
    
    for i, insight in enumerate(insights[:3], 1):
        print(f"{i}. {insight.title} (Confidence: {insight.confidence_score:.1%})")
        print(f"   Impact: {insight.business_impact}")
        print(f"   Actions: {len(insight.recommended_actions)} recommendations")
        
    # Process natural language queries
    print("\n--- Natural Language Query Processing ---")
    sample_queries = [
        "What are the sales trends over the last quarter?",
        "Predict customer churn for next month",
        "Compare performance across different product categories"
    ]
    
    for query in sample_queries:
        query_result = await platform.process_natural_language_query(query)
        print(f"Query: '{query}'")
        print(f"Response confidence: {query_result['confidence_score']:.1%}")
        print(f"Execution time: {query_result['execution_time_seconds']:.1f} seconds")
        
    # Generate comprehensive report
    print("\n--- Comprehensive Analytics Report ---")
    report = platform.generate_comprehensive_report()
    
    print(f"Data ecosystem health: {report['data_ecosystem_overview']['data_source_health']:.1f}%")
    print(f"Model performance: {report['model_performance_summary']['average_accuracy']:.1%} average accuracy")
    print(f"Optimization opportunities: {len(report['optimization_recommendations'])} recommendations")
    
    # Display top optimization recommendations
    print("\n=== Top Optimization Recommendations ===")
    for i, rec in enumerate(report['optimization_recommendations'][:3], 1):
        print(f"{i}. {rec['recommendation']} (Priority: {rec['priority']})")
        print(f"   Category: {rec['category']}")
        print(f"   Impact: {rec['impact']}")
    
    return platform, report

# Run demonstration
if __name__ == "__main__":
    import asyncio
    demo_platform, demo_report = asyncio.run(run_ai_analytics_demo())