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Big data meets the big screen: a comprehensive machine learning study about the movie industry-understanding box-office dynamics: the role of timing and audience in movie revenue predictio
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This thesis investigates factors influencing movie success using machine learning and deep learning techniques. Traditional machine learning methods analyze key determinants of box office performance, such as audience and critic ratings, release timing, and sequel dynamics. Deep learning approaches, including Natural Language Processing and Time Series Classification, examine patterns in sequential movie data. By integrating diverse data sources and predictive modeling techniques across the movie lifecycle, this study provides a comprehensive perspective on audience engagement and market dynamics. The findings advance academic understanding of the topic and offer actionable recommendations for industry stakeholders aiming to optimize performance.
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Box office success Movie success factors Traditional machine learning Natural language processing Time series analysis