Zhenyun Zhou¹ and Yu Sun², ¹Fordham College at Lincoln Center, USA, ²California State Polytechnic University, USA

This paper addresses the challenge of predicting the success of Broadway shows, a complex problem given the multifaceted nature of theater productions and their reception. Traditional methods have struggled to accurately forecast outcomes due to the dynamic interplay of factors such as audience preferences, critical reviews, and social media trends. To tackle this issue, we propose a machine learning-based model that integrates a wide range of data sources, including historical performance data, online user engagement metrics, and expert critiques [4]. Our program employs advanced data pre-processing techniques, neural network algorithms for pattern recognition, and natural language processing to analyze textual reviews and feedback [5]. During the experimentation phase, we encountered challenges related to data sparsity and variability in success criteria across different types of shows. These were mitigated by employing ensemble learning methods and customizing success metrics to align with industry standards. The application of our model across various scenarios demonstrated its versatility and improved predictive accuracy compared to existing approaches. Our findings reveal significant correlations between online engagement patterns and show success, highlighting the potential of machine learning in transforming investment and marketing strategies within the entertainment industry. Ultimately, our solution offers stakeholders a data-driven tool for decision-making, enhancing the viability and sustainability of Broadway productions. critical race aspects and offers scope for continuous innovation and improvement, demonstrating significant promise for enhancing athletic training across all levels.

Machine Learning, Natural Language Processing (NLP), Entertainment Industry, Success Prediction