• N. Sathish
• Mr.P.Yejdanikhan

This project aims to predict the average movie ticket prices for future years based on historical data. The prediction model is built using linear regression, leveraging historical data such as the number of tickets sold, total box office revenue, and inflation-adjusted box office revenue. This report details the data preprocessing steps, model training and evaluation, and the methodology used to predict future ticket prices. This report presents the development of a machine learning-based movie ticket pricing system. The report also discusses challenges such as data quality, customer acceptance, and technical implementation, and proposes solutions to address these challenges. Furthermore, it outlines potential improvements and expansion opportunities for the dynamic pricing system, including enhanced pricing algorithms, real-time market analysis, and extension to other areas such as concessions and merchandise. Overall, implementing a dynamic pricing system for movie tickets offers several benefits, including revenue maximization, improved seat utilization, enhanced customer experience, competitive advantage, and data-driven decision making.

MOVIE TICKET PRICING SYSTEM USING MACHINE LEARNING

"MOVIE TICKET PRICING SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.66-71, June-2024, Available :http://www.jetir.org/papers/JETIRGL06011.pdf

"MOVIE TICKET PRICING SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp66-71, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06011.pdf