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Open AccessJournal ArticleDOI

A Data mining Technique for Analyzing and Predicting the success of Movie

K. Meenakshi, +3 more
- Vol. 1000, Iss: 1, pp 012100
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TLDR
The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty.
Abstract
In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

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Citations
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Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

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A Flexible Query Answering System for Movie Analytics

TL;DR: A flexible query answering system (FQAS) for movie analytics that makes use of the candidate matching process to generate a prediction of a movie IMDb rating as a response to user query on movie.
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A comparative analysis of Machine Learning approaches for Movie Success Prediction

TL;DR: This paper focuses on developing an application which will help in predicting the success of the movie at early stages which would help investors to invest in movie and analysis is performed on some of the patterns from the IMDb dataset.
References
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Proceedings ArticleDOI

A Direct Method to Self-Calibrate a Surveillance Camera by Observing a Walking Pedestrian

TL;DR: By clever uses of the cross-ratio relationship in projective geometry, this method shows it is possible to directly estimate a full 3x4 camera projection matrix without first decomposing it into physical parameters like focal-length, optical center, etc.
Proceedings ArticleDOI

Recommender System Framework Using Clustering and Collaborative Filtering

TL;DR: This paper proposes a framework based on, application of data partitioning/clustering algorithm on ratings dataset followed by collaborative filtering for developing a Movie Recommender System that reduces the computation time considerably and increases the prediction accuracy.
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Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.