Build ML Model to Predict Public Theater Sessions

[BrDataRep]

Objective

Develop a machine learning model to predict which theater sessions will be public in future schedules, using data from theater booking systems.

Data Sources

• Theater booking system records
• Historical session records

Input Features

• Session time, date, and day of the week
• Theater location/venue
• Event type or genre (e.g., play, musical, concert)
• Historical attendance patterns

Tasks

1. Collect and preprocess data from theater booking systems, ensuring features include:
   • Date/time attributes
   • Venue information
   • Genre/type
   • Attendance records
2. Analyze data and engineer features suitable for prediction.
3. Define target: what constitutes a "public" session (e.g., sessions open to the general public, not private bookings).
4. Train/test various ML models (logistic regression, decision tree, etc.)
5. Evaluate performance using appropriate metrics (accuracy, precision, recall, etc.)
6. Deploy or integrate the model into theater management workflow.

Deliverables

• Data extraction and cleaning scripts
• Model training code
• Evaluation report
• (Optional) Integration code for theater systems

Notes

• Ensure compliance with data privacy policies when using real-world theater data.
• Optional: Use LLMs or the google/langextract library if extracting features from unstructured text (e.g., PDF bookings, textual records).