Description: This dataset contains information about 616 movies spanning various genres, years of release, and creative talents involved in their production. The dataset is intended for use in data analysis, visualization, and machine learning projects related to the film industry. Each row represents a single movie entry, and the dataset includes the following columns:

Movie: The title of the movie. Year: The year of release for the movie. Genres: The genres or categories associated with the movie. Certification/Rating: The film's certification or rating according to the relevant rating board or organization. IMDb ID: The unique IMDb identifier for the movie. Writer: The name(s) of the writer(s) or screenwriter(s) responsible for the movie's screenplay. Director: The name of the movie's director. Potential Use Cases:

Film industry analysis: Analyze trends in movie genres and ratings over time. Predicting movie success: Build predictive models to forecast a movie's success based on its features. Recommender systems: Develop movie recommendation systems for users based on their preferences. Creative insights: Explore relationships between directors, writers, and movie genres.

+9000 Movie Dataset

  Overview

This dataset is sourced from Kaggle and has been granted CC0 1.0 Universal (CC0 1.0) Public Domain Dedication by the original author. This means you can copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission. I would like to express our gratitude to the original author for their contribution to the data community.

  License

This dataset is released under the CC0 1.0 Universal… See the full description on the dataset page: https://huggingface.co/datasets/Pablinho/movies-dataset.

Dataset

This dataset was created by Yueming

Released under Database: Open Database, Contents: Database Contents

Contents

IMDB Movie Ratings Dataset

Evaluating Directors, Actors, Genres, and Movie Titles

By Himanshu Sekhar Paul [source]

About this dataset

This inspiring IMDB Movie Dataset is a comprehensive database of movie ratings, featuring director_name, duration, actor_2_name, genres, actor_1_name, movie title and more. Whether you're a fan of dramatic thrillers or nostalgic '90s classics from our childhoods; here you'll find information about the most voted movies from users across the world. Delve into num_voted_users trends and discover the language each movie was released in to craft your very own personal film library of country-specific titles released in any given year. With this dataset at your disposal comparing imdb scores will never be easier! Who will come out top when the votes have been tallied? Dive into data for a journey unparalleled!

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How to use the dataset

This dataset offers a comprehensive overview of the movie ratings from IMDB. It includes data about director name, duration, actors, genres, movie title, number of votes, language, country of origin, year released and IMDB score.

To use this dataset to get a deeper understanding of how movies are rated on IMDB you can take the following steps:

• Look through each column of the data to get an overall understanding. This will help you identify any specific trends or correlations in the data that you can then analyze further in later steps.
• Take some time to explore relationships between different columns such as 'Number Voted Users' and 'IMDB Score' – it could be interesting to look at how these numbers relate with each other in order better understan rating trends on IMDB?
• Analyze how particular sub-groups perform within various categories such as genre or country; this could provide insight into preferences towards certain types of movies or countries with higher associated scores than others?
• Through your analysis try and gain answers to questions related to specific demographic groups on IMDB – are there distinct preferences among age groups when it comes to what they watch? Are there any clear correlations between rating and genre within certain countries? etc…

By utilizing the questions above and taking an initial 'big picture' view before diving into more detailed analysis users should be able find value from this dataset by uncovering useful insights about movie ratings on IMDB!

Research Ideas

• Movie Recommendation System: The dataset can be used to build a movie recommendation system using machine learning algorithms like k-nearest neighbors or collaborative filtering. Based on the user's past ratings, the system can suggest relevant movies with similar genres, actors and directors.
• Movie Popularity Index: Using the data, a metric could be designed that provides an overall popularity index for movies released over the years. This index could be constructed by considering factors such as IMDb score, number of votes and reviews collected, etc..
• Genre-based Over/Under Performance Analysis: Based on genre selections in each movie year, this dataset can provide insight into which genres are performing well and which are not. This kind of analysis could help form important decisioning when deciding to allocate resources towards production budgeting or marketing campaigns for upcoming films in different genres across different regions or markets

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: movie_data.csv | Column name | Description | |:-------------------------|:---------------------------------------------------| | director_name | Name of the director of the movie. (String) | | duration | Length of the movie in minutes. (Integer) | | actor_2_name | Name of the second actor in the movie. (String) | | genres | Genre of the movie. (String) | | actor_1_name | Name of the first actor in the movie. (String) | | movie_title | Title of the movie. (String) | | num_voted_users | Number of users who voted for the movie. (Integer) | | actor_3_name | Name of the third actor in the movie. (String) | | movie_imdb_link | Link to the movie's IMDB page. (String) | | num_user_for_reviews |...

We provide a high-quality Rotten Tomatoes movie dataset that includes key metadata for thousands of movies. This dataset is ideal for anyone working with movie-related platforms, entertainment analytics, content curation, or movie discovery tools.

Our collection is structured, clean, and designed to support real-time apps, dashboards, and research use cases.

What the Dataset Includes

Each record in the dataset contains core information pulled directly from Rotten Tomatoes, including:

• Movie Name – The official title of the movie.

• Poster URL – High-resolution image link to the movie poster.

• Trailer URL – Direct link to the official trailer (when available).

• Genre – One or more genres associated with the movie, such as Action, Drama, Comedy, or Horror.

• Release Date – The date the movie was released to the public.

• Actors – Main cast members listed on Rotten Tomatoes.

• Directors – Director(s) responsible for the movie.

• Rating – Audience or critic scores, where available.

Broad Coverage

This dataset spans a wide range of movies across all major genres and decades. From modern releases to timeless classics, from Hollywood blockbusters to independent films — we’ve included movies of all types with relevant data points.

You can expect data on:

• U.S. theatrical releases

• Netflix, Amazon, and other streaming exclusives

• Festival films and limited releases

• Animated and documentary films

Use Cases

Here are just a few ways this dataset can be useful:

• Movie Recommendation Engines – Use metadata and genre info to power personalized movie suggestions.

• Entertainment Search Tools – Build searchable movie listings with visual poster previews and trailer links.

• Data Visualization Projects – Create dashboards showing trends by genre, release periods, or actor participation.

• AI/ML Training – Use metadata to train classification models or sentiment prediction tools.

• Research & Academic Use – Analyze patterns in movie releases, cast dynamics, and genre evolution.

Why Use Our Dataset?

• Clean & ready-to-use: No raw HTML, just clean structured data.

• Minimal but meaningful fields: Focused on useful movie attributes without clutter.

• Updated info: Covers both classic and current titles.

• Simple integration: Easy to use for developers, analysts, and product teams.

If you're working on a movie-based product or looking for reliable film metadata for your project, this dataset offers an ideal foundation.

Let us know if you’d like to explore it further.

Dataset Card for IMDb Movie Dataset: All Movies by Genre

  Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv. Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description… See the full description on the dataset page: https://huggingface.co/datasets/jquigl/imdb-genres.

Musss0/movie-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Movies Dataset from AllMovie is a comprehensive collection featuring over 430,000 records, encompassing a wide range of films across various genres and languages. This extensive dataset includes essential data points such as movie titles, genres, release dates, posters, languages, directors, durations, synopses, trailers, average ratings, cast information, and URLs. Such detailed metadata is invaluable for developers, researchers, and enthusiasts aiming to analyze trends, build recommendation systems, or conduct in-depth studies of the film industry.

For those interested in alternative datasets, the IMDb Non-Commercial Datasets provide subsets of IMDb data accessible for personal and non-commercial use. These datasets allow users to hold local copies of movie information, facilitating various analytical projects.

Additionally, the MovieLens datasets offer a range of movie rating data suitable for research purposes. For instance, the MovieLens 20M dataset comprises 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users, making it a valuable resource for studies in user preferences and recommendation algorithms.

Incorporating these datasets into your projects can significantly enhance the quality and depth of your analyses, providing a solid foundation for exploring various aspects of the cinematic world.

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yashvoladoddi37/movie-posters-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Title: IMDB & TMDB Movie Metadata Big Dataset (>1M)

Subtitle: A Comprehensive Dataset Featuring Detailed Metadata of Movies (IMDB, TMDB). Over 1M Rows & 42 Features: Metadata, Ratings, Genres, Cast, Crew, Sentiment Analysis and many more...

Detailed Description:

Overview: This comprehensive dataset merges the extensive film data available from both IMDB and TMDB, offering a rich resource for movie enthusiasts, data scientists, and researchers. With over 1 million rows and 42 detailed features, this dataset provides in-depth information about a wide variety of movies, spanning different genres, periods, and production backgrounds.

File Information: 1. File Size: ≈ 1GB 2. Format: CSV (Comma-Separated Values)

Column Descriptors/Key Features: 1. ID: Unique identifier for each movie. 2. Title: The official title of the movie. 3. Vote Average: Average rating received by the movie. 4. Vote Count: Number of votes the movie has received. 5. Status: Current status of the movie (e.g., Released, Post-Production). 6. Release Date: Official release date of the movie. 7. Revenue: Box office revenue generated by the movie. 8. Runtime: Duration of the movie in minutes. 9. Adult: Indicates if the movie is for adults. 10. Genres: List of genres the movie belongs to. 11. Overview Sentiment: Sentiment analysis of the movie's overview text. 12. Cast: List of main actors in the movie. 13. Crew: List of key crew members, including directors, producers, and writers. 14. Genres List: Detailed genres in list format. 15. Keywords: List of relevant keywords associated with the movie. 16. Director of Photography: Name of the cinematographer. 17. Producers: Names of the producers. 18. Music Composer: Name of the music composer.

Additional Features:

1. Unnamed 0: Index column.
2. Star1, Star2, Star3, Star4: Names of the top-billed stars.
3. Writer: Name(s) of the writer(s).
4. Original Language: Original language of the movie.
5. Original Title: Original title if different from the main title.
6. Popularity: Popularity score of the movie.
7. Budget: Budget allocated for the movie.
8. Tagline: Promotional tagline of the movie.
9. Production Companies: Companies involved in the production.
10. Production Countries: Countries where the movie was produced.
11. Spoken Languages: Languages spoken in the movie.
12. Homepage: Official website of the movie.
13. IMDB ID: Unique identifier on IMDB.
14. TMDB ID: Unique identifier on TMDB.
15. Video: Indicates if there is a video associated.
16. Poster Path: Path to the movie poster image.
17. Backdrop Path: Path to the backdrop image.
18. Release Year: Year the movie was released.
19. Collection Name: Name of the collection the movie belongs to.
20. Collection ID: Unique identifier for the collection.
21. Genres ID: Unique identifier for the genres.
22. Original Language Code: Code for the original language.
23. Overview: Brief summary of the movie.
24. All Combined Keywords: Combined keywords in a single field.

Potential Use Cases: - Sentiment Analysis: Analyze audience sentiment towards movies based on reviews and ratings. - Recommendation Systems: Build models to recommend movies based on user preferences and viewing history. - Market Analysis: Study trends in the movie industry, including genre popularity and revenue patterns. - Content Analysis: Investigate the thematic content and diversity of movies over time. - Data Visualization: Create visual representations of movie data to uncover hidden insights.

Q-b1t/IMDB-Dataset-of-50K-Movie-Reviews-Backup dataset hosted on Hugging Face and contributed by the HF Datasets community

A complete list of live websites using the Movie Database technology, compiled through global website indexing conducted by WebTechSurvey.

Unlock one of the most comprehensive movie datasets available—4.5 million structured IMDb movie records, extracted and enriched for data science, machine learning, and entertainment research.

This dataset includes a vast collection of global movie metadata, including details on title, release year, genre, country, language, runtime, cast, directors, IMDb ratings, reviews, and synopsis. Whether you're building a recommendation engine, benchmarking trends, or training AI models, this dataset is designed to give you deep and wide access to cinematic data across decades and continents.

Perfect for use in film analytics, OTT platforms, review sentiment analysis, knowledge graphs, and LLM fine-tuning, the dataset is cleaned, normalized, and exportable in multiple formats.

What’s Included:

• Genres: Drama, Comedy, Horror, Action, Sci-Fi, Documentary, and more

• Delivery: Direct download

Use Cases:

• Train LLMs or chatbots on cinematic language and metadata

• Build or enrich movie recommendation engines

• Run cross-lingual or multi-region film analytics

• Benchmark genre popularity across time periods

• Power academic studies or entertainment dashboards

• Feed into knowledge graphs, search engines, or NLP pipelines

veswaran/movie-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

mc-ai/movie dataset hosted on Hugging Face and contributed by the HF Datasets community

This file contains the features for the test portion of the movie dataset. The data has been changed into an average word vector. This is 50% of the total movie results. QUT Research Data Respository Dataset Resource available for download

contains 50

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.