Movie Success Prediction using Machine Learning Algorithms and their Comparison

Dhir, Rijul; Ramkumar, Anand

doi:10.1109/icsccc.2018.8703320

Cited by 30 publications

(19 citation statements)

References 13 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%

“…Similar to the methods mentioned above, through our paper we perform similar analytics on the IMDb dataset and create a comparison between the various models used [9], [11], and try to find an appropriate model as shown in Figure 1.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…These attributes are fed into a variety of regression [5] Machine Learning techniques [6][7] and Neural Network [8] models. We thus create a comparative study between them as done by Dhir and Raj [9] who have created a comparison specifically among Machine Learning models. Looking into the variety of models provided in the various papers [10][11], we predict the success rate [4] using a subset of these with respect to the attributes chosen earlier.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

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

“…Dhir and Raj [5], compares several machine learning algorithms -SVM, Random Forest, Ada Boost, Gradient Boost and KNN on data from IMDb. Random Forest gave the best accuracy (83%) in terms of success prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Movie Rating Prediction using Ensemble Learning Algorithms

Mhowwala¹,

Sulthana²,

Shetty³

2020

IJACSA

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Over the last few decades, social media platforms have gained a lot of popularity. People of all ages, gender, and areas of life have their presence on at least one of the social platforms. The data that is generated on these platforms has been and is being used for better recommendations, marketing activities, forecasting, and predictions. Considering predictions, the movie industry worldwide produces a large number of movies per year. The success of these movies depends on various factors like budget, director, actor, etc. However, it has become a trend to predict the rating of the movie based on the data collected from social media related to the movie. This will help a number of businesses relying on the movie industry in making promotional and marketing decisions. In this report, the aim is to collect movie data from IMDB and its social media data from YouTube and Wikipedia and compare the performance of two machine learning algorithms-Random Forest and XGBoostbest known for their high accuracy with small datasets, but large feature set. The collection of data is done from multiple sources or APIs.

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“…Therefore, in this project, we will try to unveil some of the important factors influencing the IMDb score and propose an efficient approach to predict success and failure of movie. The data we are using in our project comes from IMDb Movie Dataset on Gaggle.com [2]. It contains 28 variables for 5042 movies and 4906 posters, spanning across 100 years in 66 countries.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Movie Performance using Machine Learning Algorithms

Pawar¹

2020

IJRASET

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Movies play an important role in our everyday life. Film industry involves huge investment in terms of money, time, efforts and people. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. So, the prediction of the success of a movie is very essential to the film industry. In this proposed system, we give our detailed analysis of the Internet Movie Database (IMDb) and predict the IMDb score. This database contains categorical and numerical information such as IMDb score, director, gross, budget and so on and so forth. This system proposes a way to predict how successful a movie will be prior to its arrival at the box office instead of listening to critics and others onwhether a movie will be successful or not. The proposed system provides a quite efficient approach to predict IMDb score on IMDb Movie Dataset. We will try to unveil the important factors influencing the score of IMDb Movie Data. In the exploratory analysis we found that number of voted users, number of critics for reviews, number of Facebook likes, duration of the movie and gross collection of movie affect the IMDb score strongly. Drama and Biopic movies are best in genres.

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Movie Success Prediction using Machine Learning Algorithms and their Comparison

Cited by 30 publications

References 13 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 Rating Prediction using Ensemble Learning Algorithms

Prediction of Movie Performance using Machine Learning Algorithms

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