Film Analytics : Predicting Movies Rating Using IMDb Data
Film Analytics is a project aimed at predicting iMDB movie ratings using covariates like genre(s), actor(s), director(s), and plot keyword(s). The implemented predictor system uses and compares two regression algorithms—linear regression and ordered logistic regression (also known as ordinal logit)—for predicting the outcome rating and the predictive improvements, bringing insights about the data. We have also analyzed the trends occuring in the iMDB movie data (obtained from OMDb web API) as well as the web-scraped box office data from iMDB itself. Incorporating naive Bayes classification method, the maching learning model also has the ability to include certain plot keywords as features for predictive analysis.