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TMDB Top 10K Global Films
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About
Data detailing the top 10,000 films ranked by audience activity and ratings. The information was initially captured via The Movie Database (TMDB) APIs. This resource provides key metrics like audience ratings, vote totals, linguistic background, and plot summaries, serving as an excellent foundation for cinematic trend analysis and data science projects.
Columns
- title: The established name of the motion picture.
- overview: A textual description of the film's plot or primary concept.
- release_date: The date when the film was originally made public.
- vote_average: The calculated mean rating given by users, ranging between 5.4 and 8.7.
- vote_count: The absolute number of individual user votes recorded for the film, with a maximum exceeding 33,000.
- original_language: The primary language used during the production of the film.
- popularity: A dynamically generated index reflecting the current visibility and interest level in the film.
Distribution
The file contains 10,000 distinct records, corresponding to the highest-rated films. The dataset is delivered in CSV format under the filename 'movies-tmdb-10000.csv', and its size is approximately 3.27 MB. The structure includes 8 columns in total, covering all key metric and descriptive fields.
Usage
This resource is ideally suited for exploratory data analysis projects and model development. Suitable applications include training basic recommendation systems, performing text analysis on plot descriptions, and investigating correlations between popularity metrics, average ratings, and release dates over time.
Coverage
The dataset spans a significant historical range, featuring films released from June 10, 1895, up to February 15, 2023. The data includes titles produced in 44 unique languages, though English language films constitute a dominant 77% of the total collection. The scope is specifically limited to the 10,000 highest-rated entries tracked by the source platform.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For practising data cleaning, statistical analysis techniques, and feature engineering.
- Film Researchers: To study global cinematic performance indicators and language representation across decades.
- Machine Learning Engineers: To train models focused on content filtering, ranking prediction, or natural language processing (NLP) using the overview field.
Dataset Name Suggestions
- TMDB Top 10K Global Films
- High-Rated Movie Metrics
- Cinematic Ranking Data
- Top 10,000 Film Records
Attributes
Original Data Source: TMDB Top 10K Global Films
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