This project investigates potential bias in Fandango's movie ratings, particularly in 2015. The analysis compares Fandango's ratings with those from other platforms such as IMDB, Metacritic, and Rotten Tomatoes.
The primary goal is to analyze the data using Python, pandas, and visualization libraries like matplotlib and seaborn to uncover any systematic bias in Fandango's ratings.
This is the data behind the story Be Suspicious Of Online Movie Ratings, Especially Fandango’s openly available on 538's github: https://github.com/fivethirtyeight/data. There are two csv files, one with Fandango Stars and Displayed Ratings, and the other with aggregate data for movie ratings from other sites, like Metacritic,IMDB, and Rotten Tomatoes.
all_sites_scores.csv contains every film that has a Rotten Tomatoes rating, a RT User rating, a Metacritic score, a Metacritic User score, and IMDb score, and at least 30 fan reviews on Fandango. The data from Fandango was pulled on Aug. 24, 2015.
| Column | Definition |
|---|---|
| FILM | The film in question |
| RottenTomatoes | The Rotten Tomatoes Tomatometer score for the film |
| RottenTomatoes_User | The Rotten Tomatoes user score for the film |
| Metacritic | The Metacritic critic score for the film |
| Metacritic_User | The Metacritic user score for the film |
| IMDB | The IMDb user score for the film |
| Metacritic_user_vote_count | The number of user votes the film had on Metacritic |
| IMDB_user_vote_count | The number of user votes the film had on IMDb |
fandango_scrape.csv contains every film 538 pulled from Fandango.
| Column | Definiton |
|---|---|
| FILM | The movie |
| STARS | Number of stars presented on Fandango.com |
| RATING | The Fandango ratingValue for the film, as pulled from the HTML of each page. This is the actual average score the movie obtained. |
| VOTES | number of people who had reviewed the film at the time we pulled it. |