Analysis and Prediction of a Film's Success using Data Mining Techniques

Analysis of the driving Factors behind a Movie: Movies are loved all over the world, however, there is no particular way to predict a movie’s outcome. The paper proposes an engineered approach to predict whether a movie would be successful based on the features like the genre, cast, production company using data provided in the movie dataset from Kaggle. This analysis aims to identify the impact of the choices made during the production of a film. These choices can be picking the Director, Actors, Release Date, Genre, Language of the film, Viewer Rating etc. In particular, the aim is to understand the popularity rating of a film and identify relationships between the budget, cast, and rating of the film. From the analysis, an exploratory study of the impact that these features have on the popularity of a movie will be carried out and the choices that drive the commercial success of a movie will be sought. File Structure code: contains a Jupyter Notebook file with Python code to clean the data data: Contains the raw data downloaded from Kaggle (see below) Contains the cleaned data (output from Jupyter Notebook file) Source This dataset was generated from The Movie Database API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here