Build ML Model to Predict Public Theater Sessions

## 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).