Movie Buzz - Movie Success Prediction System Using Machine Learning Model

Sharma, Tanishq S; Dichwalkar, Riya; Milkhe, Sakshi; Gawande, Kiran

doi:10.1109/iciss49785.2020.9316087

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Furthermore, for each model, proving their statistical significance using a variety of tests [12][13] proves to be the highlight of our paper. In comparison with the accuracies provided in the papers [14] and their respective models [9], our models end up with a similar accuracy of 86% as shown through our papers.…”

Section: Literature Surveysupporting

confidence: 81%

A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

Agarwal¹,

Venugopal²,

Kashyap³

et al. 2021

Machine Learning Techniques and Data Science

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The film industry is one of the most popular entertainment industries and one of the biggest markets for business. Among the contributing factors to this would be the success of a movie in terms of its popularity as well as its box office performance. Hence, we create a comprehensive comparison between the various machine learning models to predict the rate of success of a movie. The effectiveness of these models along with their statistical significance is studied to conclude which of these models is the best predictor. Some insights regarding factors that affect the success of the movies are also found. The models studied include some Regression models, Machine Learning models, a Time Series model and a Neural Network with the Neural Network being the best performing model with an accuracy of about 86%. Additionally, as part of the testing data for the movies released in 2020 are analysed.

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Section: Literature Surveysupporting

confidence: 81%

A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

Agarwal¹,

Venugopal²,

Kashyap³

et al. 2021

Machine Learning Techniques and Data Science

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show abstract

“…Before that, we check the credibility of a model by performing various statistical tests [12][13] whose comprehensive explanation is the highlight of our paper. The best performing model in this paper gives an accuracy of 86% which holds good in comparison to the works done by Sharma et al [14] and the models used by Dhir and Raj [9].…”

Section: Literature Surveysupporting

confidence: 72%

Movie Success Prediction and Performance Comparison using Various Statistical Approaches

Agarwal¹,

Venugopal²,

Kashyap³

et al. 2022

IJAIA

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Movies are among the most prominent contributors to the global entertainment industry today, and they are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial Neural Network. The models stated above were compared on a variety of factors, including their accuracy on the training and validation datasets as well as the testing dataset, the availability of new movie characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered that certain characteristics have a greater impact on the likelihood of a film's success than others. For example, the existence of the genre action may have a significant impact on the forecasts, although another genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best performing model of all the models discussed.

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“…n the current digital age, the industry is growing rapidly, such as in the movie and technology industry [1]. According to IMDb, the average number of movies produced reached 4584 in 2005, rising to 9387 in 2015 [1].…”

Section: Introductionmentioning

confidence: 99%

Movie Recommender System Using Decision Tree Method

Azaki,

Baizal

2023

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.

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Movie Buzz - Movie Success Prediction System Using Machine Learning Model

Cited by 8 publications

References 5 publications

A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success

Movie Success Prediction and Performance Comparison using Various Statistical Approaches

Movie Recommender System Using Decision Tree Method

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