Summary of Movie Revenue Prediction Using Machine Learning Models, by Vikranth Udandarao and Pratyush Gupta
Movie Revenue Prediction using Machine Learning Models
by Vikranth Udandarao, Pratyush Gupta
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model is developed for predicting movie earnings based on various input features like title, rating, genre, release year, IMDb Rating, votes, director, writer, cast, production country, budget, company, and runtime. The structured methodology involves data collection, preprocessing, analysis, model selection, evaluation, and improvement. Trained models include Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting, and Gradient Boosting. Hyperparameter tuning and cross-validation are used for model improvement. The resulting model offers promising accuracy and generalization, enabling informed decision-making in the film industry to maximize profits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to predict how well a movie will do at the box office. That’s what this project is all about! They’re developing a special computer program that can guess a movie’s earnings based on things like its name, rating, genre, and release date. The team follows a step-by-step process to collect and prepare the data, test different models, and make improvements. The goal is to create a reliable model that can help the film industry make smart decisions about which movies to invest in. |
Keywords
» Artificial intelligence » Bagging » Boosting » Generalization » Hyperparameter » Linear regression » Machine learning » Random forest » Regression
