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Mood-Based Movie Selector Dataset
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About
Explore a vast collection of 9,718 movie records sourced from The Movie Database (TMDb) tailored to assist users in selecting films based on their individual preferences and current moods. The dataset provides crucial film attributes, including budget, revenue, and audience scores, enabling detailed analysis and the development of tailored recommendation systems. This resource facilitates the exploration of viewing habits and the discovery of films users might not typically consider.
Columns
The data is structured across multiple files containing detailed metadata:
- id: A unique identifier for each movie, used across all linked files.
- title: The name of the film.
- genres: A list detailing the associated genres.
- language: The movie's primary language.
- user_score: The average rating given by users.
- vote_count: The total number of audience votes received.
- runtime: The duration of the film, often formatted in hours and minutes.
- release_date: The original date the film was released.
- director: The name(s) of the director(s) involved in the production.
- top_billed: A list of the lead actors or actresses.
- budget_usd: The production budget of the movie in US Dollars.
- revenue_usd: The total gross revenue generated in US Dollars.
- poster_path and backdrop_path: URL links to the film’s visual media.
Distribution
The dataset encompasses 9,718 movie records. It is delivered as multiple CSV files, ensuring a structured approach to data management. Key files include
Movies.csv, FilmDetails.csv, MoreInfo.csv, and PosterPath.csv. While the data has been subjected to initial cleaning—such as ensuring high-quality data by removing duplicates and handling missing values—analysts should note that certain fields, such as financial metrics (budget and revenue), contain a notable percentage of missing values. The dataset is expected to be updated quarterly.Usage
Ideal applications include developing sophisticated mood-based recommendation algorithms to enhance users' viewing experiences. The data supports detailed exploratory data analysis (EDA), allowing researchers to visualise trends in user ratings, genres, and other film attributes to better understand audience preferences. It is also suitable for forecasting film revenue and budget performance.
Coverage
The scope covers a wide range of films catalogued by TMDb. The source data reflects global film production. The dataset focuses primarily on movie characteristics and user engagement metrics (scores, votes). Data availability notes include the fact that financial data like budget and revenue figures are not uniformly available across all 9,718 records. The expected update schedule is quarterly.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Data Scientists: To train and evaluate machine learning models for collaborative filtering or content-based recommendation systems.
- Developers: To integrate film metadata into application platforms or build discovery tools.
- Film Analysts: To study box office performance, director success rates, and genre popularity over time.
- Students/Academics: For projects focused on data mining, sentiment analysis, or media studies.
Dataset Name Suggestions
- TMDb Personalised Film Recommendation Data
- Movie Metadata and User Score Analysis
- Global Film Characteristics and Revenue Insights
- Mood-Based Movie Selector Dataset
Attributes
Original Data Source: Mood-Based Movie Selector Dataset
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