#!/usr/bin/env python3 """ Predictive Analytics for Future Event Planning Advanced predictive modeling system for concert event planning and optimization """ import json import math import base64 import hashlib import datetime import re import string import itertools import collections import dataclasses import random from typing import Dict, List, Optional, Any, Union, Tuple from dataclasses import dataclass, field from collections import defaultdict from enum import Enum class PredictionType(Enum): ATTENDANCE = "attendance" REVENUE = "revenue" COST_OPTIMIZATION = "cost_optimization" PRICING_STRATEGY = "pricing_strategy" VENUE_SELECTION = "venue_selection" ARTIST_PERFORMANCE = "artist_performance" SEASONAL_DEMAND = "seasonal_demand" MARKETING_EFFECTIVENESS = "marketing_effectiveness" class ModelType(Enum): LINEAR_REGRESSION = "linear_regression" TIME_SERIES = "time_series" ENSEMBLE = "ensemble" CLASSIFICATION = "classification" CLUSTERING = "clustering" class ConfidenceLevel(Enum): LOW = "low" # < 60% MEDIUM = "medium" # 60-80% HIGH = "high" # 80-95% VERY_HIGH = "very_high" # > 95% @dataclass class Feature: """Feature used in predictive models""" feature_id: str name: str data_type: str # numeric, categorical, temporal importance: float source: str description: str @dataclass class PredictionModel: """Predictive model configuration""" model_id: str name: str prediction_type: PredictionType model_type: ModelType features: List[str] accuracy_score: float training_data_size: int last_trained: datetime.datetime confidence_threshold: float = 0.7 @dataclass class Prediction: """Individual prediction result""" prediction_id: str model_id: str prediction_type: PredictionType predicted_value: Union[float, int, str] confidence_score: float confidence_level: ConfidenceLevel prediction_date: datetime.datetime target_date: datetime.datetime input_features: Dict[str, Any] model_version: str @dataclass class ScenarioAnalysis: """Scenario analysis for planning""" scenario_id: str name: str description: str assumptions: Dict[str, Any] predictions: List[Prediction] sensitivity_analysis: Dict[str, float] recommendations: List[str] class PredictiveAnalytics: """ Predictive analytics system for concert event planning Provides forecasting and optimization capabilities """ def __init__(self): self.models = {} self.features = {} self.predictions = defaultdict(list) self.scenarios = {} self.model_registry = self._initialize_model_registry() self.feature_importance = {} # Initialize default features self._initialize_features() def _initialize_model_registry(self) -> Dict[str, Dict[str, Any]]: """Initialize model registry with predefined model configurations""" return { "attendance_forecast": { "name": "Event Attendance Forecast", "prediction_type": PredictionType.ATTENDANCE, "model_type": ModelType.ENSEMBLE, "features": ["artist_popularity", "venue_capacity", "ticket_price", "day_of_week", "season", "weather_forecast", "marketing_spend"], "algorithm": "random_forest" }, "revenue_prediction": { "name": "Revenue Prediction Model", "prediction_type": PredictionType.REVENUE, "model_type": ModelType.LINEAR_REGRESSION, "features": ["expected_attendance", "ticket_price", "merchandise_price", "concession_prices", "vip_packages"], "algorithm": "linear_regression" }, "pricing_optimization": { "name": "Dynamic Pricing Optimization", "prediction_type": PredictionType.PRICING_STRATEGY, "model_type": ModelType.TIME_SERIES, "features": ["historical_prices", "demand_trend", "competitor_pricing", "time_to_event", "inventory_remaining"], "algorithm": "lstm" }, "cost_optimization": { "name": "Operational Cost Optimization", "prediction_type": PredictionType.COST_OPTIMIZATION, "model_type": ModelType.LINEAR_REGRESSION, "features": ["attendance", "venue_size", "staff_requirements", "equipment_needs", "duration_hours"], "algorithm": "ridge_regression" }, "artist_performance": { "name": "Artist Performance Prediction", "prediction_type": PredictionType.ARTIST_PERFORMANCE, "model_type": ModelType.CLASSIFICATION, "features": ["social_media_followers", "recent_album_sales", "streaming_numbers", "tour_history", "genre_popularity"], "algorithm": "gradient_boosting" }, "seasonal_demand": { "name": "Seasonal Demand Analysis", "prediction_type": PredictionType.SEASONAL_DEMAND, "model_type": ModelType.TIME_SERIES, "features": ["month", "holiday_indicator", "school_schedule", "local_events", "historical_bookings"], "algorithm": "seasonal_decompose" } } def _initialize_features(self): """Initialize feature definitions""" feature_definitions = [ Feature("artist_popularity", "Artist Popularity Score", "numeric", 0.9, "social_media", "Combined social media and streaming metrics"), Feature("venue_capacity", "Venue Capacity", "numeric", 0.8, "venue_data", "Maximum venue capacity"), Feature("ticket_price", "Ticket Price", "numeric", 0.7, "pricing_data", "Average ticket price"), Feature("day_of_week", "Day of Week", "categorical", 0.6, "event_schedule", "Event day (Monday-Sunday)"), Feature("season", "Season", "categorical", 0.5, "event_schedule", "Season (Winter, Spring, Summer, Fall)"), Feature("weather_forecast", "Weather Forecast", "categorical", 0.4, "weather_api", "Predicted weather conditions"), Feature("marketing_spend", "Marketing Spend", "numeric", 0.8, "financial_data", "Total marketing budget"), Feature("historical_prices", "Historical Price Data", "numeric", 0.7, "pricing_history", "Historical ticket pricing"), Feature("demand_trend", "Demand Trend", "numeric", 0.6, "sales_data", "Current demand trend indicator"), Feature("competitor_pricing", "Competitor Pricing", "numeric", 0.5, "market_data", "Average competitor pricing"), Feature("time_to_event", "Time to Event", "numeric", 0.6, "event_schedule", "Days until event"), Feature("inventory_remaining", "Inventory Remaining", "numeric", 0.7, "ticket_sales", "Remaining tickets"), Feature("social_media_followers", "Social Media Followers", "numeric", 0.8, "social_media", "Total social media followers"), Feature("recent_album_sales", "Recent Album Sales", "numeric", 0.7, "music_industry", "Recent album sales figures"), Feature("streaming_numbers", "Streaming Numbers", "numeric", 0.7, "music_industry", "Monthly streaming numbers"), Feature("tour_history", "Tour History", "numeric", 0.6, "artist_data", "Previous tour attendance averages"), Feature("genre_popularity", "Genre Popularity", "numeric", 0.5, "market_data", "Current genre popularity index"), Feature("month", "Month", "categorical", 0.4, "event_schedule", "Event month (1-12)"), Feature("holiday_indicator", "Holiday Indicator", "categorical", 0.3, "calendar", "Proximity to holidays"), Feature("school_schedule", "School Schedule", "categorical", 0.3, "calendar", "School year schedule impact"), Feature("local_events", "Local Events", "categorical", 0.4, "local_data", "Competing local events"), Feature("historical_bookings", "Historical Bookings", "numeric", 0.6, "booking_data", "Historical booking patterns"), Feature("attendance", "Expected Attendance", "numeric", 0.8, "estimates", "Expected event attendance"), Feature("venue_size", "Venue Size", "numeric", 0.7, "venue_data", "Venue square footage"), Feature("staff_requirements", "Staff Requirements", "numeric", 0.6, "operational", "Required staffing levels"), Feature("equipment_needs", "Equipment Needs", "numeric", 0.5, "technical", "Technical equipment requirements"), Feature("duration_hours", "Duration Hours", "numeric", 0.4, "event_schedule", "Event duration in hours") ] for feature in feature_definitions: self.features[feature.feature_id] = feature def create_model(self, model_config: Dict[str, Any]) -> Optional[PredictionModel]: """Create a new predictive model""" try: model = PredictionModel( model_id=model_config["model_id"], name=model_config["name"], prediction_type=model_config["prediction_type"], model_type=model_config["model_type"], features=model_config["features"], accuracy_score=model_config.get("accuracy_score", 0.0), training_data_size=model_config.get("training_data_size", 0), last_trained=datetime.datetime.now(), confidence_threshold=model_config.get("confidence_threshold", 0.7) ) self.models[model.model_id] = model return model except Exception as e: return None def predict(self, model_id: str, input_features: Dict[str, Any], target_date: Optional[datetime.datetime] = None) -> Optional[Prediction]: """Generate prediction using specified model""" if model_id not in self.models: return None model = self.models[model_id] # Validate required features missing_features = [f for f in model.features if f not in input_features] if missing_features: return None # Generate prediction using model-specific logic predicted_value = self._generate_prediction_value(model, input_features) confidence_score = self._calculate_confidence_score(model, input_features) confidence_level = self._determine_confidence_level(confidence_score) prediction = Prediction( prediction_id=f"pred_{datetime.datetime.now().timestamp()}_{random.randint(1000, 9999)}", model_id=model_id, prediction_type=model.prediction_type, predicted_value=predicted_value, confidence_score=confidence_score, confidence_level=confidence_level, prediction_date=datetime.datetime.now(), target_date=target_date or datetime.datetime.now() + datetime.timedelta(days=30), input_features=input_features, model_version="1.0" ) self.predictions[model_id].append(prediction) return prediction def _generate_prediction_value(self, model: PredictionModel, features: Dict[str, Any]) -> Union[float, int, str]: """Generate prediction value based on model type and features""" if model.prediction_type == PredictionType.ATTENDANCE: # Attendance prediction logic base_capacity = features.get("venue_capacity", 5000) artist_boost = features.get("artist_popularity", 0.5) * 0.3 price_impact = max(0.5, 1.0 - (features.get("ticket_price", 50) - 50) / 100) marketing_boost = features.get("marketing_spend", 10000) / 100000 * 0.1 predicted_attendance = base_capacity * (0.6 + artist_boost + price_impact + marketing_boost) return min(predicted_attendance, base_capacity * 0.95) elif model.prediction_type == PredictionType.REVENUE: # Revenue prediction logic attendance = features.get("expected_attendance", 3000) ticket_price = features.get("ticket_price", 75) merchandise_per_person = features.get("merchandise_price", 25) * 0.3 # 30% buy merchandise predicted_revenue = (attendance * ticket_price) + (attendance * merchandise_per_person) return predicted_revenue elif model.prediction_type == PredictionType.PRICING_STRATEGY: # Pricing optimization logic base_price = features.get("historical_prices", 75) demand_factor = features.get("demand_trend", 1.0) time_factor = 1.0 + (1.0 - features.get("time_to_event", 30) / 60) * 0.2 # Higher as event approaches inventory_factor = 1.0 + (1.0 - features.get("inventory_remaining", 0.5)) * 0.3 # Higher if low inventory optimal_price = base_price * demand_factor * time_factor * inventory_factor return optimal_price elif model.prediction_type == PredictionType.COST_OPTIMIZATION: # Cost optimization logic attendance = features.get("attendance", 3000) venue_size = features.get("venue_size", 10000) staff_hours = features.get("staff_requirements", 50) * features.get("duration_hours", 4) base_cost = venue_size * 2.0 # $2 per sq ft staff_cost = staff_hours * 25 # $25 per hour equipment_cost = features.get("equipment_needs", 10000) predicted_cost = base_cost + staff_cost + equipment_cost return predicted_cost else: # Default prediction logic return sum(features.values()) / len(features) if features else 0.0 def _calculate_confidence_score(self, model: PredictionModel, features: Dict[str, Any]) -> float: """Calculate confidence score for prediction""" base_confidence = model.accuracy_score # Adjust based on feature completeness feature_completeness = len([f for f in model.features if f in features]) / len(model.features) completeness_factor = feature_completeness * 0.3 # Adjust based on feature quality/importance total_importance = sum(self.features.get(f, Feature(f, "", "", 0.5, "", "")).importance for f in model.features if f in features) max_importance = sum(self.features.get(f, Feature(f, "", "", 0.5, "", "")).importance for f in model.features) quality_factor = (total_importance / max_importance) * 0.2 if max_importance > 0 else 0 # Model stability factor (based on training data size) data_factor = min(0.2, model.training_data_size / 10000 * 0.2) confidence = base_confidence + completeness_factor + quality_factor + data_factor return min(0.99, confidence) # Cap at 99% def _determine_confidence_level(self, confidence_score: float) -> ConfidenceLevel: """Determine confidence level from score""" if confidence_score >= 0.95: return ConfidenceLevel.VERY_HIGH elif confidence_score >= 0.80: return ConfidenceLevel.HIGH elif confidence_score >= 0.60: return ConfidenceLevel.MEDIUM else: return ConfidenceLevel.LOW def create_scenario_analysis(self, name: str, description: str, assumptions: Dict[str, Any], model_predictions: List[Tuple[str, Dict[str, Any]]]) -> Optional[ScenarioAnalysis]: """Create scenario analysis with multiple predictions""" predictions = [] for model_id, features in model_predictions: prediction = self.predict(model_id, features) if prediction: predictions.append(prediction) # Perform sensitivity analysis sensitivity_analysis = self._perform_sensitivity_analysis(predictions, assumptions) # Generate recommendations recommendations = self._generate_scenario_recommendations(predictions, sensitivity_analysis) scenario = ScenarioAnalysis( scenario_id=f"scenario_{datetime.datetime.now().timestamp()}", name=name, description=description, assumptions=assumptions, predictions=predictions, sensitivity_analysis=sensitivity_analysis, recommendations=recommendations ) self.scenarios[scenario.scenario_id] = scenario return scenario def _perform_sensitivity_analysis(self, predictions: List[Prediction], assumptions: Dict[str, Any]) -> Dict[str, float]: """Perform sensitivity analysis on predictions""" sensitivity = {} for prediction in predictions: # Simulate sensitivity by varying key input features base_features = prediction.input_features.copy() base_value = prediction.predicted_value sensitivities = [] for feature_name, feature_value in base_features.items(): if isinstance(feature_value, (int, float)): # Vary feature by ±10% varied_features = base_features.copy() varied_features[feature_name] = feature_value * 1.1 new_prediction = self.predict(prediction.model_id, varied_features) if new_prediction and isinstance(base_value, (int, float)) and isinstance(new_prediction.predicted_value, (int, float)): change = abs(new_prediction.predicted_value - base_value) sensitivity_score = change / abs(base_value) if base_value != 0 else 0 sensitivities.append((feature_name, sensitivity_score)) # Store most sensitive features if sensitivities: sensitivities.sort(key=lambda x: x[1], reverse=True) sensitivity[prediction.prediction_type.value] = { "most_sensitive": sensitivities[0][0], "sensitivity_score": sensitivities[0][1], "top_factors": sensitivities[:3] } return sensitivity def _generate_scenario_recommendations(self, predictions: List[Prediction], sensitivity: Dict[str, Any]) -> List[str]: """Generate recommendations based on scenario analysis""" recommendations = [] # Analyze predictions and generate recommendations for prediction in predictions: if prediction.prediction_type == PredictionType.ATTENDANCE: if isinstance(prediction.predicted_value, (int, float)): if prediction.predicted_value < 3000: recommendations.append("Consider increased marketing spend or lower ticket prices to boost attendance") elif prediction.predicted_value > 8000: recommendations.append("Ensure sufficient staffing and venue capacity for expected high attendance") elif prediction.prediction_type == PredictionType.REVENUE: if isinstance(prediction.predicted_value, (int, float)): if prediction.predicted_value < 200000: recommendations.append("Explore additional revenue streams (merchandise, VIP packages, concessions)") elif prediction.confidence_score > 0.8: recommendations.append("High revenue confidence - consider scaling up similar events") elif prediction.prediction_type == PredictionType.COST_OPTIMIZATION: if isinstance(prediction.predicted_value, (int, float)): attendance = prediction.input_features.get("attendance", 3000) if isinstance(attendance, (int, float)) and attendance > 0: cost_per_attendee = prediction.predicted_value / attendance if cost_per_attendee > 50: recommendations.append("Review operational efficiency to reduce per-attendee costs") # Add sensitivity-based recommendations for pred_type, sens_data in sensitivity.items(): if sens_data["sensitivity_score"] > 0.1: feature_name = sens_data["most_sensitive"] recommendations.append(f"Monitor {feature_name} closely - it significantly impacts {pred_type}") return recommendations[:5] # Limit to top 5 recommendations def get_model_performance(self, model_id: str) -> Dict[str, Any]: """Get comprehensive model performance metrics""" if model_id not in self.models: return {"error": "Model not found"} model = self.models[model_id] model_predictions = self.predictions.get(model_id, []) performance = { "model_id": model_id, "model_name": model.name, "prediction_type": str(model.prediction_type.value), "accuracy_score": model.accuracy_score, "training_data_size": model.training_data_size, "last_trained": model.last_trained.isoformat(), "total_predictions": len(model_predictions), "avg_confidence": 0.0, "feature_importance": {}, "recent_predictions": [] } if model_predictions: # Calculate average confidence avg_confidence = sum(p.confidence_score for p in model_predictions) / len(model_predictions) performance["avg_confidence"] = avg_confidence # Get recent predictions recent = sorted(model_predictions, key=lambda p: p.prediction_date, reverse=True)[:10] performance["recent_predictions"] = [ { "prediction_id": p.prediction_id, "predicted_value": p.predicted_value, "confidence_score": p.confidence_score, "prediction_date": p.prediction_date.isoformat() } for p in recent ] # Feature importance (simplified) for feature_id in model.features: if feature_id in self.features: performance["feature_importance"][feature_id] = self.features[feature_id].importance return performance def forecast_multiple_scenarios(self, base_features: Dict[str, Any], scenario_variations: List[Dict[str, Any]]) -> Dict[str, Any]: """Generate forecasts for multiple scenarios""" forecast_results = { "base_case": {}, "scenarios": {}, "comparison": {}, "generated_at": datetime.datetime.now().isoformat() } # Generate base case predictions for model_id, model_config in self.model_registry.items(): base_prediction = self.predict(model_id, base_features) if base_prediction: forecast_results["base_case"][model_id] = { "predicted_value": base_prediction.predicted_value, "confidence": base_prediction.confidence_score, "confidence_level": base_prediction.confidence_level.value } # Generate scenario variations for i, variation in enumerate(scenario_variations): scenario_name = variation.get("name", f"Scenario_{i+1}") varied_features = base_features.copy() varied_features.update(variation.get("feature_changes", {})) scenario_predictions = {} for model_id in self.model_registry.keys(): scenario_prediction = self.predict(model_id, varied_features) if scenario_prediction: scenario_predictions[model_id] = { "predicted_value": scenario_prediction.predicted_value, "confidence": scenario_prediction.confidence_score, "confidence_level": scenario_prediction.confidence_level.value } forecast_results["scenarios"][scenario_name] = { "predictions": scenario_predictions, "assumptions": variation.get("feature_changes", {}) } # Generate comparison analysis forecast_results["comparison"] = self._generate_scenario_comparison(forecast_results) return forecast_results def _generate_scenario_comparison(self, forecast_results: Dict[str, Any]) -> Dict[str, Any]: """Generate comparison analysis between scenarios""" comparison = { "best_case": {}, "worst_case": {}, "sensitivity_analysis": {} } # Find best and worst cases for each prediction type for model_id in forecast_results["base_case"].keys(): values = {} # Collect values from all scenarios values["base"] = forecast_results["base_case"][model_id]["predicted_value"] for scenario_name, scenario_data in forecast_results["scenarios"].items(): if model_id in scenario_data["predictions"]: values[scenario_name] = scenario_data["predictions"][model_id]["predicted_value"] if values: # Filter out non-numeric values for comparison numeric_values = {k: v for k, v in values.items() if isinstance(v, (int, float))} if numeric_values: best_scenario = max(numeric_values.keys(), key=lambda k: numeric_values[k]) worst_scenario = min(numeric_values.keys(), key=lambda k: numeric_values[k]) comparison["best_case"][model_id] = { "scenario": best_scenario, "value": numeric_values[best_scenario] } comparison["worst_case"][model_id] = { "scenario": worst_scenario, "value": numeric_values[worst_scenario] } return comparison def export_model_registry(self) -> Dict[str, Any]: """Export model registry and configurations""" export_data = { "models": {}, "features": {}, "export_timestamp": datetime.datetime.now().isoformat() } # Export model configurations for model_id, model_config in self.model_registry.items(): export_data["models"][model_id] = model_config # Export feature definitions for feature_id, feature in self.features.items(): export_data["features"][feature_id] = { "feature_id": feature.feature_id, "name": feature.name, "data_type": feature.data_type, "importance": feature.importance, "source": feature.source, "description": feature.description } return export_data def create_sample_scenario_features() -> Dict[str, Any]: """Create sample features for scenario testing""" return { "venue_capacity": 5000, "artist_popularity": 0.8, "ticket_price": 75.0, "marketing_spend": 15000, "day_of_week": "Saturday", "season": "Summer", "weather_forecast": "Clear", "historical_prices": 70.0, "demand_trend": 1.2, "competitor_pricing": 80.0, "time_to_event": 45, "inventory_remaining": 0.7, "expected_attendance": 4000, "venue_size": 15000, "staff_requirements": 60, "equipment_needs": 12000, "duration_hours": 4 } if __name__ == "__main__": # Initialize predictive analytics system analytics = PredictiveAnalytics() # Create models from registry print("=== Initializing Predictive Models ===") for model_id, model_config in analytics.model_registry.items(): full_config = { "model_id": model_id, "name": model_config["name"], "prediction_type": model_config["prediction_type"], "model_type": model_config["model_type"], "features": model_config["features"], "accuracy_score": random.uniform(0.75, 0.95), "training_data_size": random.randint(5000, 20000) } analytics.create_model(full_config) print(f"Created {len(analytics.models)} predictive models") # Test single predictions print("\n=== Testing Single Predictions ===") sample_features = create_sample_scenario_features() for model_id in list(analytics.models.keys())[:3]: # Test first 3 models prediction = analytics.predict(model_id, sample_features) if prediction: print(f"\n{model_id}:") print(f" Predicted Value: {prediction.predicted_value}") print(f" Confidence: {prediction.confidence_score:.2f} ({prediction.confidence_level.value})") print(f" Target Date: {prediction.target_date.strftime('%Y-%m-%d')}") # Test scenario analysis print("\n=== Scenario Analysis ===") scenario_variations = [ { "name": "High_Marketing", "feature_changes": {"marketing_spend": 25000, "ticket_price": 85.0} }, { "name": "Low_Pricing", "feature_changes": {"ticket_price": 55.0, "marketing_spend": 8000} }, { "name": "Premium_Event", "feature_changes": {"ticket_price": 120.0, "marketing_spend": 30000, "artist_popularity": 0.95} } ] forecast = analytics.forecast_multiple_scenarios(sample_features, scenario_variations) print(json.dumps(forecast, indent=2, default=str)) # Model performance print("\n=== Model Performance ===") for model_id in list(analytics.models.keys())[:2]: performance = analytics.get_model_performance(model_id) print(f"\n{model_id} Performance:") print(json.dumps(performance, indent=2, default=str)) # Export model registry print("\n=== Model Registry Export ===") registry = analytics.export_model_registry() print(json.dumps(registry, indent=2))