Comparing Filmaffinity And IMDb Ratings by Kaggle | Media and Entertainment

About this Dataset

Comparing Filmaffinity And IMDb Ratings

Comparing Filmaffinity and IMDb Ratings

Exploring Discrepancies and Factors Affecting Movie Ratings

About this dataset

This dataset is an incredibly valuable resource for anyone interested in delving deeper into the accuracy of film ratings and rankings between two popular movie websites, FilmAffinity and IMDb. This dataset analyses all movies that were listed on Filmaffinity at a given moment in time, as well as their corresponding IMDb ratings. It provides information about a wide range of data points including titles, original titles, release year, duration, country of origin, directors and writers , cast members , production companies and genres. In addition to this fascinating information about the movies themselves it also includes detailed coverage on reviews encompassing awards won for each movie achieved ratings from both sources; FilmAffinity's voted ratings along with their positive , negative and neutral votes; plus IMDb's own reviewer marks which can be compared against one another when examining predictive factors. This dataset enables researchers to make insightful visualisations over influential criteria that can successfully predict film rating results contiguously showing acceptance thresholds over time or by specific geographic region

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How to use the dataset

This dataset is a valuable resource for researchers who are interested in exploring the accuracy of FilmAffinity and IMDb's ratings, as well as the discrepancies between them. The data can be used to compare rankings positions of one platform with the other, or even to perform descriptive and predictive analyses which investigate what criteria or factors may better predict movie ratings. In order to get the most out of this dataset, it is recommended that users:

Research Ideas

Comparing the accuracy of FilmAffinity and IMDb ratings by analyzing the correlation between their numerical ratings and seeing how well it matches up with real-world customer reviews.

Developing a machine learning algorithm to accurately predict movie ratings on FilmAffinity and IMDb based on external factors such as genre, worldwide box office gross, awards, cast personnel etc.

Visualization of geographical differences in acceptance of movies over time by country or continent using data from the column “país”

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: movies.csv

Column name	Description
título	The title of the movie. (String)
título_original	The original title of the movie. (String)
año	The year the movie was released. (Integer)
duración	The duration of the movie in minutes. (Integer)
país	The country of origin of the movie. (String)
dirección	The director of the movie. (String)
guion	The writer of the movie. (String)
reparto	The cast of the movie. (String)
productora	The production company of the movie. (String)
género	The genre of the movie. (String)
sinopsis	The synopsis of the movie. (String)
premios	The awards won by the movie. (String)
puntuación_fa	The FilmAffinity rating of the movie. (Float)
votos_fa	The number of votes for the FilmAffinity rating. (Integer)
puntuación_positiva	The positive rating for the movie. (Float)
puntuación_neutral	The neutral rating for the movie. (Float)
puntuación_negativa	The negative rating for the movie. (Float)
portada_url	The URL of the movie poster. (String)
portada_local	The local path of the movie poster. (String)
puntuación_imdb	The IMDb rating of the movie. (Float)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Movies

@kaggle.thedevastator_comparing_filmaffinity_and_imdb_ratings_with_500.movies

361.93 KB
500 rows
22 columns


CREATE TABLE movies (
  "unnamed_0" BIGINT,
  "t_tulo" VARCHAR,
  "t_tulo_original" VARCHAR,
  "a_o" BIGINT,
  "duraci_n" VARCHAR,
  "pa_s" VARCHAR,
  "direcci_n" VARCHAR,
  "guion" VARCHAR,
  "reparto" VARCHAR,
  "productora" VARCHAR,
  "g_nero" VARCHAR,
  "sinopsis" VARCHAR,
  "premios" VARCHAR,
  "puntuaci_n_fa" DOUBLE,
  "votos_fa" BIGINT,
  "puntuaci_n_positiva" DOUBLE,
  "puntuaci_n_neutral" DOUBLE,
  "puntuaci_n_negativa" DOUBLE,
  "portada_url" VARCHAR,
  "portada_local" VARCHAR,
  "puntuaci_n_imdb" DOUBLE,
  "votos_imdb" DOUBLE
);