🎬 Cinema Audience Forecasting Challenge

(IITM – Kaggle Tournament)

📅 Duration: 3 Months

🎯 Objective: Predict daily audience counts for 827 theatres across India.

This repository includes the baseline notebook, the final high-scoring solution, and all original datasets used during the competition.

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📌 Problem Statement

Forecast daily audience attendance using multi-source data, combining:

• BookNow platform visits & bookings
• Theatre metadata
• Calendar features (weekday, weekend, holidays)
• Historical audience behaviour

This forms a panel time-series forecasting problem with strong seasonality, structural shifts, and theatre-level variability.

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📂 Dataset Overview

The original Kaggle dataset consisted of seven CSVs:

• cinePOS_theaters.csv – CinePOS theatre metadata
• booknow_theaters.csv – BookNow theatre metadata
• movie_theater_id_relation.csv – Mapping between CinePOS and BookNow theatres
• cinePOS_booking.csv – CinePOS bookings
• booknow_booking.csv – BookNow bookings
• booknow_visits.csv – Daily audience counts
• date_info.csv – Calendar information
• sample_submission.csv – Submission ID structure

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⚙️ Solution Approach (For Baseline Submission)

• Cleaned and explored each dataset individually
• Merged relevant files into a unified modeling dataframe
• Performed time-based train/validation split
• Experimented with multiple ML models (GBR, LightGBM, XGBoost, Random Forest)
• Applied RandomizedSearchCV for hyperparameter tuning
• Retrained the best model on the full dataset
• Created the final predictions in the required submission format