+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.

Transformed, cleaned dataset with reduced number of columns for all 45,000 movies listed in the full MovieLens dataset of movies released in July 2017 or earlier. Data points include movie ID, title, budget, languages, and genres. This dataset also includes 26 million ratings from 270,000 users for all 45,000 movies. Ratings are given on a scale of 1 to 5 and include user ID, movie ID, rating, and timestamp.

This dataset consists of the following files:

* movies.csv: The main movie metadata file. Contains information on 45,000 movies included in the full MovieLens dataset.

* ratings.csv: The full MovieLens dataset with 26 million ratings and 750,000 tag applications from 270,000 users on all 45,000 movies in this dataset.

This dataset is a further development of the following public domain dataset published on Kaggle:

https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset

This data was obtained from the official GroupLens website. The data was originally obtained from The Movies DataBase (TMDB) via the TMDB AP

R

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.

Kaggle Dataset Summary

• id
• title
• overview
• release_date
• popularity
• vote_average
• vote_count

📄 Dataset Title:

TMDB-Movie Data till 2025 (API Extract)

📋 Dataset Description / Summary:

This dataset contains top-rated movies fetched directly from the TMDB (The Movie Database) API using their /movie/top_rated endpoint. It includes detailed metadata for top-rated films across 508 pages (~10,000 movies).

The dataset is useful for:

• Exploratory data analysis
• Natural Language Processing (NLP) on movie overviews
• Recommender system projects
• Visualization or dashboard building

📌 Features:

Column Name	Description
id	TMDB unique movie ID
title	Title of the movie
overview	Textual summary/plot of the movie
release_date	Official release date (YYYY-MM-DD)
popularity	Popularity score based on TMDB algorithm
vote_average	Average user rating (0–10 scale)
vote_count	Total number of votes received

🔗 Source:

• Data fetched using TMDB API: /movie/top_rated

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.

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

Movie and TV coding of firearm use and rates per year of firearm homicide for ages 15-24

🎬 Dataset Overview

This dataset contains movie metadata including unique identifiers, titles, and genre tags. It is often used as a component of collaborative filtering and recommendation system projects.

📌 Columns

• movieId: Unique ID for each movie
• title: Movie title
• genres: Pipe-separated list of genres (e.g., Action|Drama|Comedy)

💡 Use Cases

• Content-based filtering
• Collaborative filtering (when combined with rating data)
• Genre-based recommendation models
• Exploratory data analysis on genre trends

📝 Notes

• No user or rating data included in this file
• Can be combined with rating datasets (e.g., MovieLens) for full recommendation pipelines

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

Explore the IMDB Movie Dataset to uncover trends, audience preferences, and success factors like ratings, revenue, and genres. Perfect for analysis!

Analysis of ‘IMDB Movie Dataset Latest’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ayushjain001/imdb-movie-dataset-latest on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset is being extracted from the website imdb.com using we scrapping in python( Beautiful Soup Library).It contains 1000 rows and 10 columns.

Content

This dataset contains rating of movie based on viewers review and arranged in descending order of rating using web scrap .

Inspiration

Viewer seeing this data will have an opportunity to perform various analytics technique on data and analyze the data.

--- Original source retains full ownership of the source dataset ---

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

Analysis of ‘IMDB 5000 Movie Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset on 12 November 2021.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Introduction

This data set was scraped from the site https://www.the-numbers.com/ using Python 3. it has data of more than 13k movies - and contains monetary data (Domestic Box Office, Infl. Adj. Dom. BO, Opening Weekend, and more) as well as "creative" cinema data (Comparisons, Creative Type, Genre, and more). The complete scraping code I wrote to create the data set is available in my profile: https://www.kaggle.com/code/mayasoffer/movies-data-scraper

Important Info

Please note, that the data was scraped fully from the "The-numbers" website, therefore: - There is some missing data in accordance with the missing data on the site. - The scraping was committed on 01.03.22 (March 2022) so all the data is true to that time. - For more data on how the columns were created and where the site got that data initially, please look into the site itself. - Lastly, note that I scraped the data and saved it as CSV. however, all the columns were scraped in their original form - how they were written on the website. so some "cleaning" of the columns is necessary before any analysis can take place.

Inspiration

The data is very diverse and contains a lot of different columns and goes back to 1995. so the analysis options are many. here are a few analysis leads I thought about: - How have genres changed throughout the years? what genres are the most popular throughout the years? (revenue-wise, legs, opening week...). new genres that gained popularity (animation for example) - Does MPAA rating impact revenue? and much more...

Thank you for using my dataset!

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.

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

We collected movie dataset from Internet Movie Database (IMDB) website for our experiments using an IMDbPy script to extract all the movie metadata. We obtained the box office revenues from The Movies Dataset, Box-office Mojo and The Movie Database (TMDB).These databases predominantly consisted of movies from 2006 to 2020 in various countries, and we also collected movie posters. We also used the Open Images dataset V6 for object detection of movie posters.