Abstract
The primary objective of this research work is to use prediction algorithms to forecast the success of a film in advance. In today’s world, movies have a lot of influence on investors; thus, the prediction model may assist to comprehend how well the movie will do at the box office. The goal of this study is to create a prediction model for forecasting the success of a movie in advance by using a mathematical model and mechanism based on the movie’s budget, likes and dislikes from YouTube and Twitter, and a comparison of different classification algorithms. The same dataset was used on five different classifiers, namely K-Nearest Neighbor (K-NN), Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). The paper also provides the techniques utilized along with their implementation and application. The models were trained by leveraging a good accuracy on the dataset out of which the logistic regression was found out to be the best.
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Goyal, A., Urolagin, S. (2022). Prediction of Movie Success on IMDB Database Using Machine Learning Techniques. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_20
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DOI: https://doi.org/10.1007/978-981-16-6460-1_20
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