Movies Quality Prediction Using Machine Learning Techniques

Abstract

Quality prediction plays an important role in the business outcome of the product. Due to business interest of the concept, it has extensively been studied in the last few years. Advancement in computational intelligence techniques and with the advent of robust and sophisticated machine learning algorithms, it is require to analyze the factors influencing the success of the online products. Due to busy life routine people prefer to buy products online to save their time. one of the major issue that online buyer face is that how to identify the product quality online. There are many options that can be used to identify the quality of the product and their services. Reading peoples reviews on social media platforms and applying all of the concern decision attributes on algorithms to analyze the quality. Online business is all about the predictions. stock market and Forex traders predict the commodities and currency rates then they put their lots according to the prediction. similarly other online businesses use some predictions model to estimate their sales and purchases. They all use some predictions algorithms to compute their future events and the quality to arrange resources accordingly. Movie industry is a huge market. Today, thousands of movies are released every year. This generates millions and billions dollar of revenue. There is a huge team and hard work behind each movie. The movie industry invest a heavy amount on their films to make it better and top in the box office. One of the film industry named as motion film industry situated in the United States that invest sixty millions dollar on each movie. With this huge amount of investment, they also predict good ROI. The best ROI is only possible when they make quality content which attract the viewers. When more viewers watch the movie, it will result to generate a good profit. The movie industry thoroughly study all of the concern factors that can influence the movie before starting its production. After production they give a little teaser to the viewers in terms of movie trailer and then decide when and how to release the movie. The main focus of each movie industry is on the content use in the movie and the actors that play their role. Since these two factors have a huge impact on any movie success. They also use their past experience to make their new movie more better to generate more revenue. In this work, we will apply computational intelligence techniques such as Machine Learning algorithms to predict the quality of the movie using benchmark data set, i.e. IMDB, however, this work could easily be extended to other relevant fields like identifying the quality of products etc. Our data set consist of 1000 movies witch includes different distributed attributes. The movies quality prediction is done by using blogs and people reviews on social media platforms but very less work is done by using proper attributes and features used in the movies and apply some algorithms. The prediction is based on these decision features including movie title, actor, total numbers of downloads, views, likes, Movie budget, business amount, Rating, Votes Metascore, runtime etc. Here the Rating attribute is act as a label or class. Finally, we will label the prediction results into four classes from Flop to blockbuster. The movie that gives us very high rating will be considered as blockbuster movie and the movie that gives us very bad rating will be consider as flop movie.