The movies dataset is a comprehensive collection of information about 4,803 movies. It provides a wide range of details about each movie, including budget, genres, production companies, release date, revenue, runtime, language, popularity, and more.

This dataset includes key details about each movie such as title, release year, certificate (age rating), duration in minutes, genre, IMDB rating, Metascore (a score aggregated from various critics), director, main cast, number of votes, and a brief description.

Columns Overview:

Version 1 - Title: name of the movie. - Year: year the movie was released. - Certificate: age rating given to the movie (e.g., PG, R). - Duration (min): length of the movie in minutes. - Genre: genre(s) of the movie (e.g., Action, Comedy, Drama). - Rating: IMDB user rating for the movie. - Metascore: score from critics. - Director: director(s) of the movie. - Cast: main actors in the movie. - Votes: number of votes the movie received on IMDB. - Description: brief summary of the movie's plot.

Version 2 - Poster: link of the movie poster. - Title: name of the movie. - Year: year the movie was released. - Certificate: age rating given to the movie (e.g., PG, R). - Duration (min): length of the movie in minutes. - Genre: genre(s) of the movie (e.g., Action, Comedy, Drama). - Rating: IMDB user rating for the movie. - Metascore: score from critics. - Director: director(s) of the movie. - Cast: main actors in the movie. - Votes: number of votes the movie received on IMDB. - Description: brief summary of the movie's plot. - Review Count: total count of reviews. - Review Title: title of the review. - Review: movie review (review with highest votes on it).

Possible Use Cases

1. Movie Recommendation Systems
2. Sentiment Analysis
3. Genre Analysis
4. Predictive Modeling
5. Data Visualization

This dataset is versatile and can support a wide range of analyses and applications in both academic research and practical implementations in the entertainment industry.

This dataset offers a wide-ranging collection of movie information, featuring essential details like movie titles, URLs to IMDb pages, release years, genres, and ratings. Additional data include synopses, director names, leading actors, and links to IMDb images. Compiled from IMDb, this dataset is perfect for anyone interested in movie analytics, trend analysis, or creating data-driven applications related to the film industry.

Metadata for the IMDb Movies Dataset

Certainly! Here's the updated description for the "About Dataset" section with the information about the data being scraped using Scrapy:

About the Dataset

• Title: IMDb Movies Dataset
• Subtitle: Comprehensive Movie Data: Titles, Ratings, and More
• Description: This dataset contains detailed information about movies, meticulously compiled using the Scrapy web scraping framework. It features titles, IMDb page links, images, release years, genres, ratings, and additional movie details. Each entry includes synopses, directors, and main actors, making it an invaluable resource for movie analytics and applications in the film industry.
• Keywords: Movies, Film, IMDb, Ratings, Directors, Actors, Analytics, Cinema, Data Science, Web Scraping, Scrapy
• Data Source: IMDb (Internet Movie Database)
• Date of Data Retrieval: Specify the exact date you compiled the data.
• Data Coverage: Movie details from various years, primarily focusing on recent releases.
• Geographical Coverage: Global (Movies from various countries and in multiple languages)
• Temporal Coverage: The dataset focuses on movies released in recent years (e.g., 2020-2022).
• Data Format: CSV
• Dataset Size: 54 MB
• Number of Rows: 40k
• Number of Columns: 20
• Columns and Descriptions:
  • Title: Name of the movie
  • Title_URL: IMDb URL of the movie
  • Image: Link to the movie’s poster image
  • listeritemimage_URL: Alternative IMDb URL for the movie
  • listeritemindex: Index number of the movie in the dataset
  • Year: Year of movie release
  • certificate: Movie certification (rating by age suitability)
  • Time: Runtime of the movie
  • genre: Genre(s) of the movie
  • Rating: IMDb rating of the movie
  • Score: Metascore of the movie
  • Synopsis: Brief description of the movie plot
  • Content: Detailed content including director and key actors
  • Director: Director(s) of the movie
  • Content4, Content6, Content8, Content10: Names of key actors in the movie
  • Votes: Number of votes the movie has received on IMDb
  • Gross: Gross earnings of the movie (if available)
• License: Specify the license (e.g., CC BY-SA, GPL, etc.) or state if the data is proprietary.
• Creator/Contributor: Subashanan Nair
• Version: V1
• Update Frequency: Sporadically.

The "Movie Database: Ratings, Budgets, and Box Office Earnings" is a robust SQL dataset that provides a wealth of information for movie enthusiasts, analysts, and researchers. This dataset includes essential details such as movie names, rating timelines, unique movie IDs, production budgets, viewer votes, movie types, and worldwide as well as domestic box office earnings.

****Key Features:****

Rating Timeline: Explore the ratings of movies over time, allowing you to analyze how audience perceptions change.

Budget and Box Office Data: Gain insights into the financial aspects of the movies, including production budgets and their box office performance, both domestically and globally.

Movie Classification: Understand the genres or types of movies available in the dataset, enabling you to segment and analyze different film categories.

Comprehensive Information: With movie IDs, you can easily link and query data, ensuring comprehensive and accurate analysis.

This dataset is invaluable for a wide range of applications, including movie industry analysis, trend forecasting, and understanding the financial dynamics of the film industry. Whether you're a data scientist, movie critic, or just a movie enthusiast, this dataset provides a treasure trove of information for your research and exploration.

This dataset presents a detailed snapshot of the movie industry, combining metadata from thousands of films across various genres and time periods. It includes information such as movie titles, genres, cast and crew, release dates, viewer ratings, budget, and global revenue.

Whether you're a data science enthusiast, film buff, or researcher, this dataset offers a versatile foundation for a wide range of projects — from predictive modeling and trend analysis to visual storytelling and content-based recommendation systems.

✅ Highlights: 1. Covers both classic and modern films 2. Includes structured fields for genres, cast, crew, and production data 3. Suitable for NLP tasks (overview, title analysis) 4. Designed for real-world machine learning workflows 5. Helpful for EDA, dashboards, or film popularity trends

🎯 Potential Applications: 1. Train a movie recommendation engine 2. Analyze genre popularity over decades 3. Predict box office revenue based on budget, genre, and cast 4. Visualize film industry growth or genre diversity 5. Perform text analysis on movie overviews for keywords or sentiment

If you're looking for a clean, rich, and easy-to-use movie dataset — whether for academic use, portfolio projects, or practice — this dataset is a perfect starting point.

Welcome to the "Top 1000 IMDb Movies Dataset" - a treasure trove of cinematic excellence! This comprehensive collection presents the most celebrated and beloved movies, as rated and ranked by IMDb users. Whether you're a movie enthusiast, a data scientist, or a filmmaker seeking inspiration, this dataset promises a thrilling journey through the world of cinema's finest creations.

Content:

This dataset boasts a rich array of movie attributes to help you delve into the realm of top-rated films:

Movie Name: The title of each movie, representing iconic masterpieces that have left an indelible mark on the film industry and audiences worldwide.

Year of Release: The year when each movie was released, providing valuable context for historical and chronological analyses.

Watch Time: The duration of each movie, allowing you to identify captivating films for various viewing preferences.

Movie Rating: IMDb's user ratings, serving as a benchmark for gauging audience reception and appreciation.

Metascore of Movie: Metascores from renowned critics, offering insights into the films' critical acclaim and recognition.

Gross Earnings: The worldwide box office earnings, reflecting the commercial success and popularity of each movie.

Votes: The number of votes cast by IMDb users, indicating the films' popularity and reach.

Description: Brief summaries that provide a glimpse into the captivating plots and themes of these cinematic marvels.

Usage:

The "Top 1000 IMDb Movies Dataset" opens up a world of possibilities for movie aficionados and data enthusiasts alike:

Film Analysis and Insights: Dive into data-driven analyses to uncover intriguing patterns, trends, and correlations among highly-rated movies.

Rating Predictions: Develop predictive models to estimate movie ratings based on other attributes and gain a deeper understanding of factors influencing user perceptions.

Box Office Performance: Examine the relationship between critical acclaim, ratings, and box office success, providing valuable insights for filmmakers and producers.

Genre Exploration: Investigate the distribution of top-rated movies across different genres to identify trends in audience preferences.

Temporal Trends: Analyze changes in movie ratings, box office performance, and viewer preferences over time to reveal shifts in cinematic taste and trends.

Caution:

While this dataset is a treasure trove for movie analysis, it is essential to respect copyright and intellectual property rights. Users are encouraged to use the data responsibly and comply with IMDb's terms of use and any applicable legal restrictions.

Conclusion:

The "Top 1000 IMDb Movies Dataset" offers an immersive and data-driven exploration of cinema's greatest works, igniting creativity, fostering discussions, and enriching our appreciation for the art of storytelling. Let the magic of these cinematic gems captivate you as you embark on an exciting journey into the world of acclaimed movies!

The Kaggle Movies dataset is available in CSV format and consists of one file: "movies.csv".

The file contains data on over 10,000 movies and includes fields such as title, release date, director, cast, genre, language, budget, revenue, and rating. The file is approximately 3 MB in size and can be easily imported into popular data analysis tools such as Excel, Python, R, and Tableau.

The data is organized into rows and columns, with each row representing a single movie and each column representing a specific attribute of the movie. The file contains a header row that provides a description of each column.

The file has been cleaned and processed to remove any duplicates or inconsistencies. However, the data is provided as-is, without any warranties or guarantees of accuracy or completeness.

The "movies.csv" file in the Kaggle Movies dataset includes the following columns:

id: The unique identifier for each movie. title: The title of the movie. overview: A brief summary of the movie. release_date: The date when the movie was released (in YYYY-MM-DD format). Popularity: A numerical score indicating the relative popularity of each movie, based on factors such as user ratings, social media mentions, and box office performance. Vote Average: The average rating given to the movie by users of the IMDb website (on a scale of 0-10). Vote Count: The number of ratings given to the movie by users of the IMDb website.

🎬 Movie Dataset

This dataset contains 20,000+ movies, information suitable for data analysis, machine learning projects. The dataset consists of 14 columns with a mix of boolean, numerical, and textual features.

🧾 Column Descriptions

Context

These files contain metadata for all 45,000 movies listed in the Full MovieLens Dataset. The dataset consists of movies released on or before July 2017. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages.

This dataset also has files containing 26 million ratings from 270,000 users for all 45,000 movies. Ratings are on a scale of 1-5 and have been obtained from the official GroupLens website.

Content

This dataset consists of the following files:

movies_metadata.csv: The main Movies Metadata file. Contains information on 45,000 movies featured in the Full MovieLens dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies.

keywords.csv: Contains the movie plot keywords for our MovieLens movies. Available in the form of a stringified JSON Object.

credits.csv: Consists of Cast and Crew Information for all our movies. Available in the form of a stringified JSON Object.

links.csv: The file that contains the TMDB and IMDB IDs of all the movies featured in the Full MovieLens dataset.

links_small.csv: Contains the TMDB and IMDB IDs of a small subset of 9,000 movies of the Full Dataset.

ratings_small.csv: The subset of 100,000 ratings from 700 users on 9,000 movies.

The Full MovieLens Dataset consisting of 26 million ratings and 750,000 tag applications from 270,000 users on all the 45,000 movies in this dataset can be accessed here

Acknowledgements

This dataset is an ensemble of data collected from TMDB and GroupLens. The Movie Details, Credits and Keywords have been collected from the TMDB Open API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here.

The Movie Links and Ratings have been obtained from the Official GroupLens website. The files are a part of the dataset available here

https://www.themoviedb.org/assets/static_cache/9b3f9c24d9fd5f297ae433eb33d93514/images/v4/logos/408x161-powered-by-rectangle-green.png" alt="">

Inspiration

This dataset was assembled as part of my second Capstone Project for Springboard's Data Science Career Track. I wanted to perform an extensive EDA on Movie Data to narrate the history and the story of Cinema and use this metadata in combination with MovieLens ratings to build various types of Recommender Systems.

Both my notebooks are available as kernels with this dataset: The Story of Film and Movie Recommender Systems

Some of the things you can do with this dataset: Predicting movie revenue and/or movie success based on a certain metric. What movies tend to get higher vote counts and vote averages on TMDB? Building Content Based and Collaborative Filtering Based Recommendation Engines.

Description

This dataset contains information about 10,000 movies, including their titles, release dates, popularity scores, ratings, vote counts, and plot summaries. It can be used for data analysis, visualization, recommendation systems, sentiment analysis, and machine learning projects related to movies.

Dataset Information

• Number of Rows: 10,000
• Number of Columns: 8

Columns

1. Unnamed: 0 – Index column generated during dataset creation.
2. id – Unique movie identifier.
3. title – Name of the movie.
4. release_date – Official release date of the movie.
5. overview – Short plot summary or description of the movie.
6. popularity – Popularity score assigned to the movie.
7. vote_average – Average user rating of the movie.
8. vote_count – Total number of user votes received.

Possible Use Cases

• Exploratory Data Analysis (EDA)
• Movie recommendation systems
• Popularity prediction models
• Rating prediction using machine learning
• Natural Language Processing (NLP) on movie overviews
• Data visualization and dashboard projects
• Trend analysis of movies across different years

This dataset offers a rich collection of metadata for [insert number, e.g., 45,000] movies, compiled from [insert source, e.g., The Movie Database (TMDb), IMDb, or other]. It captures a wide range of attributes, including movie titles, genres, release dates, budgets, revenues, cast, crew, production details, and user ratings. Designed for data enthusiasts, researchers, and film industry analysts, this dataset is a valuable resource for exploring cinematic trends, building predictive models, or developing recommendation systems.

Content

IMDb is a goldmine for those who are learning the concepts of Data and ML/AI. The dataset has been scraped to meet one's need with various personalised touches such as the addition of movie posters, descriptions and runtime.

Most Christmas Movies Ever Made* 🎄

By IMDB

Includes 784 movies found on IMDB

Columns:

*These are movies that IMDB has tagged as a 'Christmas Movie'. This dataset does not include every Christmas movie and it also includes movies that some might find more subjectively Christmas related. The goal is to eventually have every movie included here.

More Datasets

For more datasets, click [here]

Featured Notebooks

🚨 Your notebook can be here! 🚨!

Description:

• The dataset comprises anonymized data on movies available on IMDb, capturing various aspects such as genre, rating, and revenue.
• This dataset can be utilized for analyzing movie trends, audience preferences, and the impact of different attributes like genre and director on movie success.
• It can aid in understanding the factors contributing to high ratings and box office revenue, as well as providing insights into the popularity of genres over time.
• This dataset can be utilized for analyzing movie success factors, audience preferences, and genre trends.
• It can help in identifying the relationship between movie features (e.g., genre, director) and ratings or revenue, examining the popularity of actors and directors, and understanding critical reception through Metascore.

Features:

This dataset is about movies from 2000 to 2020 Columns : name, year, genre, description, director, stars name - name of the movie year - release year genre - type of movie description - information about movie director - name of the director stars - movies lead actors and actress

This dataset contains 9,140 top-rated movies collected from the TMDB (The Movie Database) API. It includes essential information such as movie titles, overviews, release dates, ratings, vote counts, and popularity scores.

The dataset is suitable for:

Movie recommendation systems Exploratory Data Analysis (EDA) Natural Language Processing (NLP) on movie descriptions Data visualization Machine Learning projects Popularity and rating analysis Columns | Column | Description | | -------------- | --------------------- | | id | Unique TMDB movie ID | | title | Movie title | | overview | Short movie synopsis | | release_date | Movie release date | | vote_average | Average user rating | | vote_count | Number of user votes | | popularity | TMDB popularity score |

Note: The unamed [0] index column is simply an index column and can be ignored or removed after loading the dataset.

Welcome to the Movie Recommendation Dataset! This dataset is curated for building recommendation systems in the fascinating world of movies. Whether you're a data scientist, machine learning enthusiast, or a movie buff, this dataset provides a rich collection of information about various movies, offering endless possibilities for analysis and recommendation system development.

Data Source: This dataset is compiled from various reliable sources and offers a diverse collection of movies across different genres, time periods, and regions. It is suitable for both beginners looking to explore the basics of recommendation systems and advanced users aiming to develop sophisticated algorithms.

Acknowledgments: We would like to express our gratitude to the contributors and data sources that made this dataset possible. Their dedication to providing high-quality movie data has enabled the creation of a resourceful dataset for the Kaggle community.

Introduction

The Bollywood movies dataset, stored in a CSV file named "movies.csv," encapsulates a wealth of information about various Hindi films. This dataset is a result of scraping data from IMDb, the renowned online movie database.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6286349%2F819e5482c6da1a86dec2b6e8ff56e589%2FScreenshot%202023-12-06%20141838.png?generation=1701868749160899&alt=media" alt="">

Each entry in the dataset provides details about a Bollywood movie, including its title, release year, duration, ratings, number of raters, a brief summary, and the total votes it has received. In this exploration, we will delve into the significance of each column, unraveling the cinematic landscape of these films.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6286349%2Fc2a53559afec2e64ed8b0259e85d16db%2FScreenshot%202023-12-06%20141719.png?generation=1701868761401120&alt=media" alt="">

Columns and Subtitles:

https://media.giphy.com/media/3otPoBgmnFsekQF4B2/giphy.gif" alt="">

Film Name:

Represents the title of the Bollywood movie.This column serves as a key identifier for each entry, providing a quick reference to the film.

https://media.giphy.com/media/26grAKoxuP9XkPqve/giphy.gif" alt="">

Year:

Denotes the release year of the movie.The year of release is crucial in understanding the historical context and timeline of Bollywood cinema.

https://media.giphy.com/media/3o6wrq8VYeGXuQTGZa/giphy.gif" alt="">

Duration:

Specifies the length of the movie in hours and minutes.Duration is an essential aspect for viewers, influencing their decision to engage with the film based on available time.

https://media.giphy.com/media/l1KsqdtB1kB6FCfok/giphy.gif" alt="">

Ratings:

Indicates the IMDb rating given to the movie.Ratings reflect the critical and audience reception, providing insights into the overall quality and appeal of the film.

https://media.giphy.com/media/3owzVVzNoi1hBoIgqQ/giphy.gif" alt="">

Number of Raters:

Represents the quantity of individuals who have rated the movie on IMDb.The number of raters is a measure of the film's popularity and the extent of its reach among the audience.

https://media.giphy.com/media/3ohfFs6InH1536JDt6/giphy.gif" alt="">

Summary:

Offers a concise overview of the movie's plot or theme.The summary provides a glimpse into the narrative, offering potential viewers a preview of the storyline.

https://media.giphy.com/media/3o6wrvQjlvTpppQWiI/giphy.gif" alt="">

Votes:

Displays the total count of votes received by the movie on IMDb.The votes signify the level of engagement and interaction from the audience, reflecting the film's impact.

https://media.giphy.com/media/3ofT5Hf1mLziHtLco8/giphy.gif" alt="">

Exploration:

The dataset presents a fascinating opportunity to explore the dynamics of Bollywood cinema, unveiling trends in ratings, popularity, and the evolving nature of film themes. Analyzing the interplay between ratings, number of raters, and votes can offer valuable insights into audience preferences and the success of these movies in the digital era.

https://media.giphy.com/media/3XsniM1pgA5R6ZkCeX/giphy.gif" alt="">

As we embark on this cinematic journey through the dataset, we'll uncover patterns, outliers, and intriguing correlations that shed light on the diverse landscape of Bollywood movies in recent years.

Description:

This dataset is an extensive collection of the top 400 movies in various genres, spanning from January 1, 2000, to January 1, 2024, as cataloged on IMDb. It serves as a significant resource for analyzing trends and patterns in the film industry over the last two decades.

Check the simple EDA Code:** EDA IMDB TOP 400**

Content:

The dataset comprises two main components:

Merged Dataset: This part consolidates the top 400 movies for each genre. The columns include:

Genre: The genre of the movie. Movie Name: The title of the movie. Year: The release year or range (for series). Rating: IMDb rating, including the total number of votes. Votes: Total votes the movie received. Type: Differentiates between movies, TV series, and mini-series. Detail/Main Story: A brief summary or storyline of the movie. Metascore: The metascore rating, if available. Raw Dataset (Raw_Dataset_IMDBTop400): This segment contains detailed information about each movie in individual genre-specific files (e.g., Adventure.xlsx). It includes additional data like poster links and IMDb URLs not present in the merged dataset.

For interested users, the RAW dataset has been provided. Inside Raw_Dataset_IMDBTop400 folder you may find:

Links to movie posters. IMDb URLs of the movies. Movie titles with their sequence number in the list. Release year or range. Content rating (e.g., TV-MA, PG-13). IMDb rating with total votes. Other details, such as series type, summary, metascore, etc. Potential Uses:

This dataset can be employed for various analyses, such as:

Trends in movie genres over the years. Rating patterns in relation to release years or movie types. Analysis of popularity based on votes and IMDb ratings. Exploring narrative themes and summaries in modern cinema. Note to Users:

If you find this dataset useful, please consider upvoting. For an example of detailed exploratory data analysis (EDA) on a similar IMDb dataset, you can visit this Kaggle link.

Additional Information:

The Raw_Dataset_IMDBTop400 folder is particularly useful for in-depth, genre-specific analysis. The merged dataset is ideal for quick overviews and general analysis. This dataset is a rich resource for movie enthusiasts, data analysts, researchers, and anyone interested in the dynamics of the film industry.

Movies Performance and Feature Statistics

Analyzing Box Office Performance, Rating and Audience Reactions

By Yashwanth Sharaff [source]

About this dataset

This dataset contains essential characteristics of a variety of movies, including basic pieces of information such as the movie's title and budget, as well as performance indicators like the movie's MPAA rating, gross revenue, release date, genre, runtime, rating count and summary. With this data set we can better understand the film industry and uncover insights on how different features and performance metrics impact one another to guarantee a movie's success. The movies dataset also helps you make informed decisions about which features are key indicators in setting up a high-grossing feature film

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get the most out of this data set you need to understand what each column in it represents. The ‘Title’ column gives you the title of the movie which can be used for further search or exploration on popular streaming services and websites that are dedicated to providing detailed information about movies. The ‘MPAA Rating’ lists any Motion Picture Association (MPAA) rating for a movie which consists of G (General Audiences), PG (Parental Guidance Suggested), PG-13 (Parents Strongly Cautioned), R (Under 17 Requires Accompanying Parent or Guardian) etc. The 'Budget' column give you an approximate idea about how much a particular production cost while the 'Gross' columns depicts its earnings if it was released in theaters while its successor 'Release Date' reveals when each film has been released or is going to release in future. The columns 'Genre', 'Runtime', and ‘Rating Count’ cover subje​cts such as what type of movie is it? Every genre will have an associated runtime limit along with rating count which refers to number people who have rated/reviewed a particular flick whether on IMDB or other streaming services as well as paper mediums like newspapers . Last but not least summary field states an overview of what we can expect from film so take this in account before watching anything especially if include children members in your family.

So go ahead - start exploring this interesting dataset today!

Research Ideas

• Creating a box office prediction model using budget, genre, release date and MPAA rating
• Using the summary data to create a sentiment analysis tool for movie reviews
• Building a recommendation engine for users based on their prior ratings and what other users with similar tastes have rated as highly

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: movies.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------------------| | Title | The title of the movie. (String) | | MPAA Rating | The Motion Picture Association of America (MPAA) rating of the movie. (String) | | Budget | The budget of the movie in US dollars. (Integer) | | Gross | The gross revenue of the movie in US dollars. (Integer) | | Release Date | The date the movie was released. (Date) | | Genre | The genre of the movie. (String) | | Runtime | The length of the movie in minutes. (Integer) | | Rating Count | The number of ratings the movie has received. (Integer) | | Summary | A brief summary of the movie. (String) |

Acknowledgements

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