Female moviegoers aged 18 to 34 years old represented ** percent of the audience of movies released in the first half of 2024 in the United States and Canada. The data shows that younger consumers went to the movies more often, as the audience share dropped sharply among viewers aged 35 years old and older.

The statistic shows the audience distribution of Get Out in the United States in 2017, by age. According to the source, approximately ** percent of the audience was aged 25 to 34.

In 2019, there were *** million frequent moviegoers aged 60 or above, up from *** million in the previous year. Conversely, more 12 to 17-year-olds were visiting the cinema regularly that year. Why do some people go to movie theaters less than they used to? There is a clear overall change in movie-going frequency among U.S. adults – ** percent of respondents to a 2018 survey said that they saw fewer movies in theaters than five years ago. Whilst many consumers still prefer to see movies in theaters upon their release, the hobby is arguably less popular than it used to be. Trips to the cinema can be costly – on average, a ticket to a North American movie theater cost **** U.S. dollars in 2018. For many Americans, this is too expensive, especially with streaming services like Netflix, Hulu, and Amazon offering subscribers better value for money. It is no coincidence that the appeal of the movie theater has waned as streaming services have grown in popularity. It is now the norm to subscribe to a combined TV and movie streaming service, and whilst the **** dollar fee for a movie theater ticket covers just one showing, several streaming platforms allow consumers to enjoy content whenever and wherever they want for similar price per month. Equally, a movie must appeal to film fans before they go to see it (particularly if they consider the price higher than they would like) and if for several months nothing comes up that interests them, it is only natural that they will visit theaters less regularly. Again, the appeal of a movie often largely depends on age. Far more younger adults are likely to watch live-action Disney remakes like ‘The Lion King’ than their older peers, for instance – and younger viewers also watch dystopian content more than older generations.

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

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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.

Film Genre Statistics

Movie genre statistics and revenue data from 1995-2018

By Throwback Thursday [source]

About this dataset

This dataset contains genre statistics for movies released between 1995 and 2018. It provides information on various aspects of the movies, such as gross revenue, tickets sold, and inflation-adjusted figures. The dataset includes columns for genre, year of release, number of movies released in each genre and year, total gross revenue generated by movies in each genre and year, total number of tickets sold for movies in each genre and year, inflation-adjusted gross revenue that takes into account changes in the value of money over time, title of the highest-grossing movie in each genre and year, gross revenue generated by the highest-grossing movie in each genre and year, and inflation-adjusted gross revenue of the highest-grossing movie in each genre and year. This dataset offers insights into film industry trends over a span of more than two decades

How to use the dataset

Understanding the Columns

Before diving into the analysis, let's familiarize ourselves with the different columns in this dataset:

• Genre: This column represents the genre of each movie.
• Year: The year in which the movies were released.
• Movies Released: The number of movies released in a particular genre and year.
• Gross: The total gross revenue generated by movies in a specific genre and year.
• Tickets Sold: The total number of tickets sold for movies in a specific genre and year.
• Inflation-Adjusted Gross: The gross revenue adjusted for inflation, taking into account changes in the value of money over time.
• Top Movie: The title of the highest-grossing movie in a specific genre and year.
• Top Movie Gross (That Year): The gross revenue generated by the highest-grossing movie in a specific genre and year.
• Top Movie Inflation-Adjusted Gross (That Year): The inflation-adjusted gross revenue of the highest-grossing movie in a specific genre and year.

Analyzing Data

To make use of this dataset effectively, here are some potential analyses you can perform:

• Find popular genres: You can determine which genres are popular by looking at columns like Movies Released or Tickets Sold. Analyzing these numbers will give you insights into what types of movies attract more audiences.

• Measure financial success: Explore columns like Gross, Inflation Adjusted Gross, or Top Movie Gross (That Year) to compare the financial success of different genres. This will allow you to identify genres that generate higher revenue.

• Understand movie trends: By analyzing the dataset over different years, you can observe trends in movie releases and gross revenue for specific genres. This information is crucial for understanding how movie preferences change over time.

• Identify highest-grossing movies: The column Top Movie gives you the title of the highest-grossing movie in each genre and year. You can use this information to analyze the success of specific movies within their respective genres.

Data Visualization

To enhance your analysis, consider using data visualization techniques

Research Ideas

• Predicting the popularity and success of movies in different genres: By analyzing the data on tickets sold and gross revenue, we can identify trends and patterns in movie genres that attract more audiences and generate higher revenue. This information can be useful for filmmakers, production studios, and investors to make informed decisions about which genres to focus on for future movie releases.
• Comparing the performance of movies over time: With the inclusion of inflation-adjusted figures, this dataset allows us to compare the box office success of movies across different years. We can analyze how movies in specific genres have performed over time in terms of gross revenue and adjust these figures for inflation to get a better understanding of their true financial success.
• Analyzing the impact of genre popularity on ticket sales: By examining the relationship between genre popularity (measured by tickets sold) and total gross revenue, we can gain insights into audience preferences and behavior. This information is valuable for marketing strategies, as it helps determine which movie genres are most likely to attract a larger audience base and generate higher ticket sales

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...

In 2024, around ** percent of the cinema visitors in China were between 20 and 29 years old. Moviegoers aged above 40 years accounted for ** percent of the total audience.

The "Real Movies Dataset" offers a comprehensive repository of diverse movie information, facilitating in-depth analysis and meaningful comparisons across various cinematic attributes. With its wealth of key details, this dataset serves as an invaluable resource for researchers, enthusiasts, and industry professionals alike. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18544731%2Fbfb64d5c16fa1164befbde46928b7f83%2FMovies%20Kaggle.jpg?generation=1707490228580924&alt=media" alt=""> Each entry in the dataset includes the following attributes: * Movie Name: The title of the movie. * Year of Release: The year in which the movie was officially released to the public. * Watch Time: The duration of the movie in terms of hours and minutes, indicating the length of time required to watch the entire film. * Movie Rating: This refers to the rating assigned to the movie based on various criteria such as content, suitability for different age groups, and overall quality. Ratings could be numerical (e.g., out of 10). * Meatscore of Movie: This is a unique metric that represents the "meatiness" or substance of the movie. It might be a score assigned based on the complexity of the plot, character development, thematic depth, or other qualitative aspects. * Votes: The number of votes or ratings received by the movie from viewers or critics. This metric provides an indication of the movie's popularity or reception. * Gross: The total box office gross earnings generated by the movie, typically measured in a specific currency (e.g., USD). This metric reflects the commercial success of the film. * Description: The dataset includes a brief description field providing a summary or overview of the movie's plot, genre, themes, or notable aspects. This description offers context and insight into the content and style of each film, aiding in understanding and analysis.

Overall, the "Real Movies Dataset" serves as a valuable resource for researchers, analysts, and enthusiasts interested in exploring and studying the dynamics of the film industry, including trends in movie production, audience preferences, and financial performance.

Cinema Advertising Market Outlook

According to our latest research, the global cinema advertising market size stood at USD 4.38 billion in 2024, reflecting a steady resurgence post-pandemic. The market is projected to reach USD 7.82 billion by 2033, expanding at a robust CAGR of 6.5% during the forecast period from 2025 to 2033. This growth is driven by the revival of the global film industry, increasing consumer footfall in cinemas, and the rising effectiveness of immersive, large-format advertising. As per our latest analysis, cinema advertising continues to be a compelling channel for brands seeking high-impact, captive audience engagement in an era marked by digital ad saturation and growing consumer fatigue with online ads.

A primary growth factor for the cinema advertising market is the unique ability of cinema environments to deliver high-impact, undistracted brand messaging. Unlike digital platforms where viewers can easily skip or ignore ads, cinema audiences are a captive group, offering advertisers a rare opportunity for undivided attention. The cinematic experience, characterized by large screens, superior sound, and immersive visuals, amplifies the emotional resonance of advertisements, making them more memorable and effective. Furthermore, the resurgence of blockbuster releases and event movies has led to increased foot traffic in theaters, providing a larger and more diverse audience base for advertisers to target. The trend of exclusive movie premiers and themed screenings has further augmented the value proposition for brands, as these events often attract high-income demographics and niche audiences, driving premium ad rates and enhanced ROI for cinema advertising campaigns.

Another significant driver is the rapid integration of digital technologies and data analytics into cinema advertising. Modern cinema chains are leveraging advanced projection technologies, dynamic content delivery systems, and audience analytics to offer advertisers more precise targeting and creative flexibility. Programmatic advertising solutions are making inroads into the cinema space, enabling brands to tailor their messages based on movie genre, show timings, and audience demographics. This data-driven approach not only improves the relevance and impact of ads but also provides measurable insights into campaign performance. Additionally, the adoption of interactive and augmented reality (AR) elements in on-screen and off-screen advertising is elevating audience engagement levels, creating new opportunities for brands to connect with consumers in innovative and memorable ways.

The cinema advertising market is also benefiting from the evolving media strategies of both local and global brands. As television and online advertising become increasingly fragmented, brands are seeking alternative channels to build brand recall and emotional connection. Cinema advertising, with its premium context and ability to reach both urban and suburban audiences, is emerging as a critical component of integrated marketing campaigns. Retail, automotive, entertainment, and food & beverage sectors are particularly active, using cinema ads to launch new products, promote seasonal offers, and reinforce brand values. Moreover, the rise of luxury and experiential branding is driving demand for high-quality, visually stunning ad creatives that leverage the unique canvas of the cinema screen.

Cinema POS Systems have become an integral part of the modern cinema experience, enhancing operational efficiency and customer satisfaction. These systems streamline ticketing, concessions, and customer interactions, allowing cinema operators to deliver a seamless and enjoyable experience for moviegoers. By integrating with loyalty programs and mobile apps, Cinema POS Systems enable personalized promotions and offers, driving repeat visits and boosting revenue. As cinemas continue to adopt digital solutions, the role of POS systems in managing inventory, tracking sales, and analyzing consumer preferences becomes increasingly important. This technological advancement not only improves the operational capabilities of cinemas but also enhances the overall customer journey, making it a vital component in the competitive landscape of cinema advertising.

Regionally, Asia Pacific leads the cinema advertising market, fueled by rapid urbanization, a booming film industry, an

In 2021, approximately ** percent of moviegoers in the United States and Canada identified as Caucasian and/or White. Viewers who identified as Hispanic and/or Latino accounted for ** percent of the total.

Detailed Description:

This dataset records detailed information about ticket sales and customer behavior at a cinema hall, offering insights into various aspects such as demographics, movie genre preferences, seat selection, ticket pricing, and customer retention patterns. It is designed to help analyze customer engagement, spending behavior, and factors that influence repeat visits to the cinema. The data is useful for predictive modeling and can support decision-making processes related to customer retention, marketing strategies, and optimizing cinema operations.

Columns Overview:

1. Ticket_ID (Categorical):

   • Description: A unique alphanumeric identifier for each ticket purchase. The ID consists of a random uppercase letter (A-Z) followed by a 4-digit number (e.g., B7539, Y1344).
   • Significance: This column helps identify each individual transaction. It's a categorical variable, essential for tracking specific customer purchases but not related to other variables directly.

2. Age (Numerical):

   • Description: The age of the customer who purchased the ticket, ranging between 18 and 60 years.
   • Significance: Age is an important demographic feature, providing insights into customer segments. For example, younger audiences might prefer different movie genres or seating types compared to older customers. Analyzing age data can help cinema halls cater to the needs of various age groups.

3. Ticket_Price (Numerical):

   • Description: The price the customer paid for the ticket, typically ranging from $10 to $25. The price varies based on factors like movie time, seat type, or cinema location.
   • Significance: Ticket price reflects customer spending and the cinema's pricing strategy. Understanding how ticket pricing impacts customer behavior can help optimize ticket sales and maximize revenue.

4. Movie_Genre (Categorical):

   • Description: The genre of the movie the customer attended, which can include one of the following: Action, Comedy, Horror, Drama, or Sci-Fi.
   • Significance: Genre preferences are crucial for understanding customer interests. Analyzing which genres are most popular can guide movie scheduling, marketing strategies, and even help in curating personalized recommendations for customers.

5. Seat_Type (Ordinal):

   • Description: The type of seat selected by the customer, with three ordinal categories:
     • Standard (Basic seating option)
     • Premium (Enhanced seating with added comfort)
     • VIP (Exclusive seating, offering premium features like extra legroom and priority service)
   • Significance: Seat type provides insights into customer spending behavior. Premium and VIP seat types typically correlate with higher ticket prices, and understanding seat preferences can help in optimizing cinema layout and pricing strategies. Additionally, this column can be used to gauge the popularity of high-end seating options.

6. Number_of_Person (Mixed Variable):

   • Description: The number of people accompanying the customer. This can either be:
     • Alone: The customer attended alone.
     • 2–7: The customer attended with a group of 2 to 7 people.
   • Significance: Group size is an important factor in understanding customer preferences and behavior. For example, groups might purchase more tickets or opt for different movie genres and seat types than solo attendees. This column is crucial for analyzing social dynamics and group behavior in cinema attendance.

7. Purchase_Again (Target - Binary):

   • Description: A binary target variable indicating whether the customer is likely to return and purchase another ticket. It has two possible values:
     • Yes: The customer is likely to return for another movie.
     • No: The customer is not likely to return.
   • Significance: This is the key column for predictive modeling. It is used to assess customer retention and predict the likelihood of future ticket purchases. Analyzing the factors that influence repeat purchases (e.g., age, genre preferences, seat types) helps cinema halls optimize marketing and customer engagement strategies.

Use Cases & Applications:

1. Customer Segmentation:
   By analyzing variables like Age, Movie_Genre, and Seat_Type, cinema halls can identify different customer segments. For example, young customers may prefer Action or Comedy genres, while older customers may prefer Drama or Sci-Fi. This segmentation can guide personalized marketing campaigns, ticket discounts, and loyalty programs.

2. Customer Retention Analysis:
   The Purchase_Again column is crucial for assessing customer loyalty. By correlating it with other factors like **Ticket_Price...

Dataset Description
The Netflix Life Impact Dataset (NLID) is a meticulously curated collection of 80+ transformative films, designed to uncover how cinematic experiences leave lasting emotional, intellectual, and behavioral imprints on audiences. Each entry provides:

Basic Attributes: Title, genre, release year, average rating, and review volume.
Transformative Insights: A precise timestamp for the “Minute of Life-Changing Insight” and the actionable advice viewers derived from it.
Audience Engagement: Discovery channels (e.g., friend recommendations, social media), the percentage of viewers who shared the film’s lesson, and qualitative review highlights.

This dataset transcends traditional movie analytics by spotlighting the human impact of storytelling—how scenes spark introspection, alter perspectives, or inspire action. It bridges cinema studies, psychology, and data science, making it invaluable for understanding art’s role in shaping human behavior.

Context and Inspiration
As a data scientist and lifelong film enthusiast, I noticed a critical gap: most movie datasets focus on box office metrics or superficial ratings, ignoring why certain stories resonate deeply. This project began with a simple question: What makes a film unforgettable?

💡 Did a scene ever redefine your worldview? That fleeting moment when a character’s struggle mirrors your own, or a line of dialogue becomes a mantra—this dataset captures those universal yet deeply personal catalysts for change.

I spent months analyzing thousands of reviews, cross-referencing critical essays, and identifying recurring themes in viewer testimonials. From Oscar-winning dramas to cult classics, every entry reflects rigorous validation to ensure authenticity and relevance.

Sources and Methodology
Accuracy is paramount. Data was manually aggregated and verified using:
- Streaming Platforms: Netflix, Amazon Prime, and Hulu for ratings, discovery trends, and audience demographics.
- Audience Feedback: IMDb, Reddit, and Letterboxd reviews to pinpoint pivotal scenes and extract life lessons.
- Critical Analyses: Academic journals and film critiques to validate the cultural significance of highlighted moments.

Every “life-changing minute” and its associated advice underwent cross-validation against multiple sources to ensure universality. For example, Parasite’s flood scene (1:12:00) was flagged by 85% of reviewers as a commentary on invisible privilege.

Key Features
1. Emotional Metrics:
- Life-Changing Timestamp: Exact minute marking the film’s transformative moment (e.g., Whiplash’s drumming finale at 1:20:00).
- Meaningful Advice: Concise takeaways viewers adopted (e.g., Coco’s “Honor your roots”).

2. Audience Behavior:
- Discovery Channels: How viewers found the film (e.g., 92% of The Pursuit of Happyness viewers were referred by friends).
- Shareability: Percentage of viewers who recommended the film’s lesson (e.g., 97% for Klaus).

3. Rigorous Curation:
- Each entry synthesizes quantitative metrics (ratings, reviews) with qualitative depth (review highlights, psychological impact).

Potential Use Cases
✅ Storytelling Analysis: Identify which genres (e.g., documentaries like The Social Dilemma) or themes (e.g., resilience, systemic injustice) most influence audiences.
✅ Personalized Recommendations: Build systems that suggest films based on life lessons (e.g., “Persistence pays off” for motivational content).
✅ Cultural Psychology: Study how societal issues (e.g., class inequality in Parasite) shape collective emotional responses.
✅ Content Creation: Guide filmmakers in crafting impactful scenes by analyzing timestamp patterns.

License
This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You may:
- Use, adapt, and share the data for any purpose.
- Attribute the work to me, noting modifications.

Details: CC BY 4.0 License.

Why This Dataset Stands Out
1. Dual Lens: Combines hard metrics (e.g., 92% Y recommendation rate for Paddington 2) with human-centric insights (e.g., “Always choose kindness”).
2. Cross-Disciplinary Utility: Appeals to data scientists, psychologists, filmmakers, and educators.
3. Passion-Driven Precision: Every entry reflects hours of manual review, ensuring depth and credibility.

Summary
The Netflix Life Impact Dataset (NLID) isn’t just about movies—it’s about the moments that redefine us. Whether you’re training an AI to predict cultural trends, studying the psychology of art, or seeking films that challenge your worldview, this dataset illuminates the invisible threads between storytelling and human transformation. Lights, camera, impact. 🎬

Rotten Tomatoes Top Movies Ratings and Technical Data

Film Critic and User Reviews

By [source]

About this dataset

This dataset captures the success remarkable movies that have been rated highly by both critics and viewers alike. Here, you'll find a comprehensive collection of technical data on each film, covering everything from box office figures, production crews and credits, airing dates and lengths of time for each film. However, the best part of it all are the ratings given out by both critics and viewers; these ratings will definitely help you pick out your favorite movie from this remarkable pool of films! With this dataset we hope to give people insight into how top-rated movies fared in terms of viewing numbers, user scores and critical reviews - so what better way to find your next all-time favorite movie than here?

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Featured Notebooks

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How to use the dataset

This dataset includes technical data and ratings for 900+ top-rated movies according to critics and users of Rotten Tomatoes. It is an excellent resource for anyone interested in researching some of the highest rated films, as well as exploring various topics such as film trends, filmmaking techniques, and more.

Research Ideas

• Creating a movie recommendation app using the critic and user reviews. The app will use the ratings from this dataset to recommend similar movies that are highly rated by users and critics.
• Analyzing trends in top-rated movies over time by examining year of release, total number of reviews, total ratings, and box office gross from this dataset.
• Performing sentiment analysis on critical reviews to detect the top-rated films with positive or negative review sentiment for each movie genre listed in this dataset

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: rotten_tomatoes_top_movies.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------------| | title | The title of the movie. (String) | | year | The year the movie was released. (Integer) | | synopsis | A brief summary of the movie. (String) | | critic_score | The score given to the movie by critics. (Integer) | | people_score | The score given to the movie by viewers. (Integer) | | consensus | A summary of the reviews for the movie. (String) | | total_reviews | The total number of reviews for the movie. (Integer) | | total_ratings | The total number of ratings for the movie. (Integer) | | type | The type of movie (e.g. feature film, documentary, etc.). (String) | | genre | The genre of the movie (e.g. action, comedy, etc.). (String) | | original_language | The original language of the movie. (String) | | director | The director of the movie. (String) | | producer | The producer of the movie. (String) | | writer | The writer of the movie. (String) | | release_date_(theaters) | The date the movie was released in theaters. (Date) | | release_date_(streaming) | The date the movie was released for streaming. (Date) | | box_office_(gross_usa) | The total box office gross in the USA. (Integer) | | runtime | The length of the movie in minutes. (Integer) | | production_co | The production company of the movie. (String) | | sound_mix | The sound mix used for the movie. (String) | | aspect_ratio | The aspect...

The TMDb (The Movie Database) is a comprehensive movie database that provides information about movies, including details like titles, ratings, release dates, revenue, genres, and much more.

This dataset contains a collection of 1,000,000 movies from the TMDB database.

Dataset is updated daily. If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Interesting Task Ideas:

1. Predict movie ratings based on features such as revenue, popularity, genre, and runtime.
2. Identify trends in movie release dates and analyze their impact on revenue.
3. Analyze the relationship between budget, revenue, and popularity to determine factors that contribute to a movie's success.
4. Build a recommendation system that suggests similar movies based on genres, production companies, and language.
5. Perform sentiment analysis on movie reviews to understand audience reactions.
6. Explore the impact of movie genres on popularity and revenue.
7. Investigate the correlation between runtime and audience engagement.
8. Identify successful production companies and analyze their strategies.
9. Utilize natural language processing techniques to extract meaningful insights from movie overviews.
10. Visualize movie popularity over time and identify popular genres in different periods.

Checkout my other datasets

Clash of Clans Clans Dataset 2023 (3.5M Clans)

Black-White Wage Gap in the USA Dataset

130K Kindle Books

USA Unemployment Rates by Demographics & Race

150K TMDb TV Shows

Photo by Onur Binay on Unsplash

The data contain the experiences of selected Kenyan film audience of slow cinema. The data was conducted through a web survey and at the time of data collection, no such study had been conducted within a Kenyan cinema context. The data could be useful to researchers interested in film audience analyses, examining the situatedness of slow cinema in an African (Kenyan) context, and understanding how a film audience visualizes and interpretes difficult cinema. Lastly, the data could provide insights into the demographics of the Kenyan film audience.

The global movie rating sites market is experiencing robust expansion, projected to reach a substantial market size of approximately $1,500 million by the end of 2025, with a compelling compound annual growth rate (CAGR) of around 15% anticipated through 2033. This growth is fueled by an increasing reliance on aggregated reviews and audience scores for movie selection, a phenomenon particularly pronounced among younger demographics and in markets with burgeoning digital entertainment consumption. The market is intricately linked to the health and output of the film industry itself, where effective movie promotion and insightful audience choice analysis are paramount for success. Platforms like Rotten Tomatoes, IMDb, and Metacritic have become indispensable tools for both consumers and industry professionals, shaping box office performance and influencing production decisions. The shift towards data-driven marketing in the entertainment sector further underscores the vital role these rating sites play in understanding viewer sentiment and identifying emerging trends. Key drivers of this market include the proliferation of streaming services, which has led to an explosion of content and a greater need for curation and discovery tools. Consumers actively seek out trusted sources to navigate this vast landscape, making user ratings and professional reviews more influential than ever. The market is segmented by application, with "Movie Promotion" and "Audience Choice" emerging as dominant segments, reflecting their direct impact on a film's commercial viability. By type, "Based On User Ratings" and "Professional Ratings" are the most significant, showcasing the dual importance of aggregated public opinion and expert critical analysis. Emerging markets, particularly in the Asia Pacific region, are poised to contribute significantly to future growth, driven by rapid digitalization and a growing appetite for global cinema. While the market enjoys strong tailwinds, potential restraints could arise from the proliferation of fake reviews, the challenge of maintaining impartiality, and the increasing dominance of a few major players, leading to potential consolidation. This comprehensive report offers an in-depth analysis of the global Movie Rating Sites market, providing critical insights into its evolution, current landscape, and future trajectory. Spanning the Study Period of 2019-2033, with a Base Year of 2025 and a detailed Forecast Period of 2025-2033, this report meticulously examines the market dynamics throughout the Historical Period of 2019-2024. Leveraging millions of data points and expert analysis, it aims to equip stakeholders with actionable intelligence for strategic decision-making.

The Movies dataset is a comprehensive collection of movie-related information designed to provide valuable insights into various films. It includes the following key attributes:

Identifiers: Unique identifiers for each movie, ensuring easy reference and retrieval. Title and Overview: Essential details about the movie, including its title and a brief summary. Ratings and Votes: Information on the movie's average rating and total votes, reflecting audience reception. Financials: Data on the movie's budget and revenue, allowing analysis of its financial performance. Release and Status: Details about the release date and current status (e.g., released, upcoming). Demographics: Indicators of whether the movie is suitable for adults. Language and Production: Information on the original language, production companies, and countries involved in the film's creation. Genres and Popularity: Classification of the movie into genres and a popularity score, providing context on its cultural impact. Overall, this dataset serves as a valuable resource for analyzing trends, performance, and characteristics of films within the cinema industry.

Dataset using TMDB API, where there's 10,000 rows of data of movie's name, description, and genre.'

Potential use cases for this dataset:

1. Movie Genre Classification: The dataset can be used to train machine learning models for automatically predicting or classifying movie genres based on textual information such as overviews. This can help in building recommendation systems, genre-based search engines, or genre-based movie recommendation algorithms.

2. Genre-based Movie Analysis: Researchers or movie enthusiasts can analyze the dataset to gain insights into the distribution of different genres, identify trends, or study genre-specific characteristics. For example, they can explore the popularity of specific genres over time or analyze the relationships between different genres.

3. Content Recommendation: The dataset can be utilized to recommend movies to users based on their genre preferences. By understanding the genre composition of movies and users' past preferences, recommendation systems can suggest relevant movies that align with users' tastes.

4. Genre-specific Audience Targeting: Movie studios and production companies can use the dataset to understand the demographics and preferences of different genre-specific audiences. This information can assist in targeting specific genres to particular audience segments during marketing and distribution campaigns.

5. Sentiment Analysis: Researchers can use the dataset's overviews to perform sentiment analysis to understand the general sentiment associated with different genres. This can provide insights into how viewers perceive and respond to different movie genres.

By leveraging the columns in your "Movie Genre Detection" dataset, you can explore various aspects of movie genres, automate genre classification, offer personalized movie recommendations, and gain valuable insights into audience preferences and sentiments.

The IMDb movie dataset and IMDb rating dataset provide comprehensive information about movies listed in the IMDb database along with their ratings. Here's a brief description of each:

1. IMDb Movie Dataset:

   • This dataset contains detailed information about movies, including their titles, original titles, release years, publication dates, genres, durations, countries of production, languages, directors, writers, production companies, actors, descriptions, average votes, total votes, budgets, gross incomes (both in the USA and worldwide), metascores, and reviews from users and critics.
   • Each row represents a unique movie entry in the IMDb database, identified by its IMDb title ID.
   • The dataset offers insights into various aspects of movies, such as their production details, cast and crew, plot summaries, and reception through ratings and reviews.

2. IMDb Rating Dataset:

   • This dataset provides ratings and voting statistics for movies listed in the IMDb database.
   • It includes information such as IMDb title IDs, weighted average votes, total votes, mean and median votes, distribution of votes across different rating levels (from 1 to 10), and average votes and votes counts segmented by gender and age groups.
   • Additionally, it offers ratings from specific voter groups, such as top 1000 voters, US voters, and non-US voters.
   • Each row corresponds to a movie entry in the IMDb database, identified by its IMDb title ID.
   • The dataset enables analyses of movie ratings across different demographics and provides insights into the audience's perception and reception of movies listed on IMDb.

IMDB Movie Dataset Description: 1. imdb_title_id: Unique identifier for each movie in the IMDb database. 2. title: Title of the movie. 3. original_title: Original title of the movie (may differ from the localized title). 4. year: Year of release of the movie. 5. date_published: Date when the movie was published or released. 6. genre: Genre(s) to which the movie belongs. 7. duration: Duration of the movie in minutes. 8. country: Country or countries where the movie was produced or filmed. 9. language: Language(s) spoken in the movie. 10. director: Director(s) of the movie. 11. writer: Writer(s) of the screenplay or story for the movie. 12. production_company: Production company or companies involved in producing the movie. 13. actors: Main actors or cast members of the movie. 14. description: Brief description or summary of the movie's plot or storyline. 15. avg_vote: Average rating or vote score given to the movie by IMDb users. 16. votes: Total number of votes received by the movie on IMDb. 17. budget: Budget allocated for producing the movie. 18. usa_gross_income: Gross income or revenue generated from the movie's release in the United States. 19. worlwide_gross_income: Gross income or revenue generated from the movie's release worldwide. 20. metascore: Metascore rating assigned to the movie by critics (if available). 21. reviews_from_users: Number of user reviews or ratings submitted for the movie. 22. reviews_from_critics: Number of reviews or ratings given by critics for the movie.

IMDB Rating Dataset Description: 1. imdb_title_id: Unique identifier for each movie in the IMDb database. 2. weighted_average_vote: Weighted average rating or vote score given to the movie. 3. total_votes: Total number of votes received by the movie. 4. mean_vote: Mean or average vote score given to the movie. 5. median_vote: Median vote score given to the movie. 6. votes_10: Number of votes rating the movie as 10. 7. votes_9: Number of votes rating the movie as 9. 8. votes_8: Number of votes rating the movie as 8. 9. votes_7: Number of votes rating the movie as 7. 10. votes_6: Number of votes rating the movie as 6. 11. votes_5: Number of votes rating the movie as 5. 12. votes_4: Number of votes rating the movie as 4. 13. votes_3: Number of votes rating the movie as 3. 14. votes_2: Number of votes rating the movie as 2. 15. votes_1: Number of votes rating the movie as 1. 16. allgenders_0age_avg_vote: Average vote score given by viewers of all genders in the 0-18 age group. 17. allgenders_0age_votes: Number of votes from viewers of all genders in the 0-18 age group. 18. allgenders_18age_avg_vote: Average vote score given by viewers of all genders in the 18-30 age group. 19. allgenders_18age_votes: Number of votes from viewers of all genders in the 18-30 age group. 20. allgenders_30age_avg_vote: Average vote score given by viewers of all genders in the 30-45 age group. 21. allgenders_30age_votes: Number of votes from viewers of all genders in the 30-45 age group. 22. allgenders_45age_avg_vote: Average vote score given by viewers of all genders in the 45+ age group. 23. allgenders_45age_votes: Number of votes from viewers of all genders in the 45+ age group. 24. males_allages_avg_vote: Average vote score given by male view...

Comedy Film Market size was valued at USD 45.9 Billion in 2024 and is projected to reach USD 69.4 Billion by 2032, growing at a CAGR of 5.3% during the forecast period 2026 to 2032. The Comedy Film Market is driven by the rising global demand for light-hearted entertainment and stress-relief content across diverse audiences. Increasing popularity of streaming platforms and digital distribution has widened access, boosting viewership and revenues for comedy films. Growing investments in original productions, cross-cultural comedy, and celebrity-driven content are further fueling market expansion. Additionally, social media promotion and audience engagement trends enhance visibility and drive higher box office and subscription-based growth.

Movies on Streaming Platforms Dataset

This dataset contains detailed information about movies that are available on various streaming platforms, which has been thoroughly gathered via web scraping techniques.

Content Details

• The data includes unique identifiers for each movie, the title of the film, the release year, and age rating, alongside the Rotten Tomatoes score.
• It specifies whether a movie is available on major streaming platforms such as Netflix, Hulu, Prime Video, and Disney+.
• With 9515 unique values for movie titles, the dataset covers a wide range of cinematic works, from classics to recent releases.
• The dataset spans over a century of movie releases, from as early as 1914 to 2021.

Use Cases and Inspiration

• The dataset can be used to quickly identify on which streaming platforms a specific movie title is available, aiding viewers in their content selection.
• Researchers and marketers can analyze the dataset to find trends in the types of movies offered by different platforms, their release years, and the critical reception reflected by Rotten Tomatoes scores.
• One could explore the relationship between the target age group of movies and the streaming services they are found on, providing insights into platform content strategies.
• The dataset can serve as a foundation for recommendation systems, market competition analysis between streaming services, and studies on the impact of age ratings on a movie's streaming presence.

Column Descriptions

• ID: A unique identifier for each movie within the dataset.
• Title: The full title of the movie as it appears on the streaming platforms.
• Year: The release year of the movie, indicating when the movie was first made available to the public.
• Age: The recommended age group for the movie's audience, such as '7+', '13+', '16+', or '18+'.
• Rotten Tomatoes: The movie's score on Rotten Tomatoes, which reflects critics' reviews and can be used as a measure of the movie's reception.
• Netflix: A binary indicator (0 or 1) of whether the movie is available on Netflix, with 1 indicating availability.
• Hulu: A binary indicator (0 or 1) of whether the movie is available on Hulu.
• Prime Video: A binary indicator (0 or 1) of whether the movie is available on Amazon Prime Video.
• Disney+: A binary indicator (0 or 1) of whether the movie is available on Disney+.
• Type: A categorical indicator distinguishing the content as either a 'Movie' or a 'TV Show'.

Additional Dataset Attributes

• Each row in the dataset represents a single movie, with its corresponding attributes spread across the aforementioned columns.
• The dataset is comprehensive and potentially updated regularly, ensuring a current snapshot of movies across major streaming platforms.
• There are columns for additional streaming platforms, which can be included in the analysis as needed.
• Some entries may have missing values, especially in age ratings, which could be due to various factors such as the movie being unrated or newly released.