Prediction of movie popularity

Rad my post on Medium: Linear and bayesian modelling in R: Predicting movie popularity

Introduction

Movie popularity can help people decide which movie to watch, or whether they want to go the cinema to watch it or wait till the DVD is release and watch it at home. Consequently, it could also help theater owner to choose which movies to show or how many times to show it or for how long.

Purpose

The purpose of this project is to analysis a dataset containing information about movies and create a linear model and a bayesian model to predict movie popularity.

This repository includes the following main files:

• a dataset movies.Rdata used to perform the analysis.
• a README.md that explains how all of the scripts work and how they are connected.
• a Rmd file that shows the raw code of the linear regression and bayesian modeling analysis called movies_prediction.Rmd.
• a htlm file that shows the analysis that was performed called movies_predictions.html.

Data Set Information

In the movies dataset, there is 651 randomly sampled movies which were released in United States movie theater in the period of 1970-2014. The data was obtained from Rotten Tomatoes and IMDB. The dataset contains 32 features of each movie, including genre, MPAA rating, production studio, and whether they recieved Oscar nominations.

Even though there is no detailed information about the exact sampling methods used, the movies included in this dataset were randomly sampled from the above two mentioned sources and no bias were created by the sampling method so we can assume that the results obtained can be generalized to all U.S movies released between 1970 and 2014. On the other hand, because this is an observational study, the relationships that could be find from this data indicate association, but not causation

Analysis

movies_predictions.Rmd perform the following tasks:

• Reads the movies.Rdata
• Exploratory Data Analysis:
  • Creates new variables that summarizes other variables
  • Obtains descriptive statistics summarizing central tendency, dispersion and shape of the dataset’s distribution
  • Creates visuals in order to explore the relationships existent in the dataset
  • Evaluates relationships and distribution of original and new variables created.
• Linear regression part:
  • Creates a linear model that selects the best parameters to predict movie popularity using backwards elimination
  • Evaluates the linear model performance by testing the 4 assumptions of a linear model
• Bayesian modeling part:
  • Creates model to detect best predictors of movie popularity under Bayesian assumptions
  • Performs graphical summaries and model diagnosis