Machine Learning Models for Predicting Movie Ticket Pricing

Predicting the price of a movie ticket is essential in the dispensations of data analytics and changed customer entertainment behavior. Increasing demand for high-quality 3D screen movies is undergirded by advancements in movie digitization technology in visual effects, driving the movie theatre market. Watching movies with family and friends in cinemas is one of the best entertainment mediums after having a hectic schedule. However, it is difficult to go somewhere without knowing how much you will pay for a specific movie, hence the need for tools to address this customer-side demand. People need to see the prices of tickets online at any time. On the other hand, the movie proprietor aims to make maximum revenue. This study proposes a system modeled to predict the price of the ticket for a specific movie and provided with validations to ensure consistency, reliability, and, most importantly, the correctness of the information. Movie ticket pricing was predicted using different machine learning algorithms to model our system and compare performance to determine which algorithm has the most accurate results. Random forest, Decision trees, and KNN had the highest accuracy.