Machine Learning-Based Attendee Behavior Prediction and Event Personalization System

Develop an ML-powered platform that predicts participant behavior, suggests personalized sessions, and improves engagement at conferences and seminars using historical data and real-time interactions.

Collect and preprocess historical event attendance, session preferences, and participant behavior data.

Design database schemas to store participant profiles, event details, and interaction logs.

Implement ML models for predicting attendance patterns, popular sessions, and individual preferences.

Build a frontend interface to display personalized event suggestions for participants.

Integrate backend services for real-time data collection, ML inference, and recommendation updates.

Develop dashboards for organizers to track predicted vs. actual engagement trends.

Test ML model accuracy, system performance, and recommendation relevance.

Deploy the platform on cloud servers to handle multiple events simultaneously.

Document ML methodology, database design, and system implementation details.