Prediction of box office revenue of movies
The volatile nature of the film industry has rendered box office prediction a challenging task. Factors like social media buzz and ever changing trends have left film producers and studios scrambling for a foolproof method for predicting box office revenue to mitigate risks. Leveraging a rich and...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175059 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The volatile nature of the film industry has rendered box office prediction a challenging task.
Factors like social media buzz and ever changing trends have left film producers and studios scrambling
for a foolproof method for predicting box office revenue to mitigate risks. Leveraging a rich and
comprehensive dataset comprising information relating to 36,000 movies sourced from popular movie
sites such as TMDb and IMDb, this study aims to analyse the importance of feature engineering on the
accuracy of predictor models. Apart from features such as budget, this study explores the use of sentiment
analysis to determine audience reactions to the trailer of a movie. Four distinct models: Random Forest,
LightGBM, Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) were analysed
and their performance compared against each other to determine which model best suits the task of box
office prediction. |
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