- How to Access the Project
- Introduction
- Project Objective
- About the Data
- Project Overview
- Conclusion
To obtain the best rendering results and make sure all the internal links work properly, it is recommended to open the provided notebook using the Jupyter nbviewer service. A direct link to the notebook rendered this way is provided clicking here.
Movies are one of the most universal forms of entertainment; people in every part of the world crowd into theaters waiting for the projector's light to hit the big screen. Before the COVID pandemic stroke in 2020, the global yearly revenue generated by the movie industry was estimated to be $41.7 billion.
The myriad of different stories to tell, the creative freedom the cinematographic art allows, is arguably what really makes movies so attractive. Beneath this romantic view, however, we need to keep in mind that film studios are in business to make a profit; for the most part, the prospect of multimillion revenues is really what convinces stakeholders to invest in the production of a flick.
In this project we use machine-learning modeling to predict movie box office gross revenues; to reach this goal, we rely on features such as budgets, genres, release date and popularity.
Along the way, through data wrangling and exploratory analysis, we unveil interesting statistical facts involving movies. By the end of the project, we have a clear idea of what are the factors that more greatly influence the revenue of a film.
To train our models we use a variety of metadata about 3,000 different movies. The dataset containing this information can be downloaded from this Kaggle page and was originally obtained using the API of The Movie Database (TMDB), a popular website which collects extensive data about motion pictures and TV shows.
The project is divided into 3 main steps:
- Step 1: Loading the Data and Identifying the Target Variable.
- Step 2: Data Wrangling, Exploratory Analysis and Feature Engineering: this is the most extensive part and it is functional to getting the data ready for modeling.
- Step 3: Predictive Modeling and Performance Evaluation: here we actually train the regression models and predict the movie revenues; models' performance is evaluated using the Logarithmic Root Mean Squared Error metric (LMSE).
After analyzing and visualizing the data, we found out that:
- Movie revenues show a strong correlation with movie budgets.
- The Avengers, Furious 7 and Avengers: Age of Ultron are the three movies with highest box office gross.
- About 20% of the movies we have available are part of a collection/series. This kind of movies tend to have higher revenues.
- All movies we consider have been released between 1921 and 2017. The year with the most number of movie releases is 2013.
- September, October and December are the most popular months for movie releases; Friday is the most popular day.
- Drama is the most frequent genre, followed by Comedy and Thriller. The most profitable genres, however, are not necessarily the most frequent. Among the top ones we find Adventure and Animation; on the bottom there are Foreign Movies.
We used machine learning to predict the values of the log transformed revenues. We trained four different types of models: a Linear Regression, a Ridge Regression, a Random Forest and a Gradient Boosting regressor. After comparing their performances, we concluded that the most accurate predictions were achieved through Gradient Boosting. In this case we obtained a LMSE value of 1.4438.
The features that had the biggest impact when making predictions were revenue and popularity score.