This dataset provides a detailed analysis of global box office performance from 2000 to 2024. It includes information on movies released during this period, covering key metrics such as release dates, genres, production budgets, worldwide gross, and more. The dataset aims to assist researchers, data scientists, and movie enthusiasts in exploring trends in the film industry, analyzing profitability, and understanding audience preferences over the years.

Key Features: 1. Timeframe: 2000–2024 2. Metrics: Revenue, production budget, profit margins, and more 3. Genres: Covers various genres to analyze trends in audience preferences 4. Insights: Ideal for trend analysis, profitability studies, and forecasting

This dataset is ideal for: - Machine learning projects such as predicting box office success - Exploratory data analysis (EDA) for trends in the movie industry - Research on the evolution of filmmaking economics

Note: All data is curated from publicly available sources.

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...

This data set supports the analyses presented in the paper titled Quantifying the global film festival circuit: Networks, diversity, and public value creation, published in PLoS ONE: https://doi.org/10.1371/journal.pone.0297404. The R code to reproduce the analyses is available at: https://github.com/andreskarjus/cinandofestivals. The data sample is sufficient for the reproduction of results and graphs. The graphs in this paper are also available as an interactive dashboard in an online supplementary, where details behind individual data points can be easily observed: https://andreskarjus.github.io/cinandofestivals.This research has been made possible by data provided directly to authors from the Cannes Film Market (Marché du Film – Festival de Cannes), the company operating the Cinando website and database (https://cinando.com/). Launched in 2003 as the database of the attendees at the Cannes Festival, Cinando has since grown into the premier platform supporting hundreds of film festivals and film markets (industry events held during festivals, mostly oriented to promoting investment opportunities, rights sales, and production services). Cinando offers film professionals tools to navigate the film industry, including information about contacts, films, projects in development, market screening schedules, market attendees, and screeners. The platform services film festivals and markets by facilitating rights sales, investments, and business-to-business video on demand. The platform relies on a large proprietary relational database. The authors received a full database dump via a research partnership with the data owner on 2021-10-08.This data set concerns the part of the Cinando database that registers information about film festival programming and contains, at face value, 77,398 films programmed at 38,367 festival events, resulting in 183,865 film–festival event pairs, between 2007–2022 (festivals_films.csv). The festival metadata includes event and, occasionally, festival series title, event location country, and event year. Film metadata contains runtime, production year, names of crew members (filmrole.csv - only crew role and gender are made available to protect privacy), origin countries (filmcountry.csv), languages spoken in the film (filmlang.csv), and content type tags (filmgen.csv). The latter is a mixture of tags typically used to describe films within the festival context, including genre (e.g. drama, documentary), target audience (children’s, family), identity (Jewish, LGBT), and production type (TV Series, VR). The authors have cleaned and homogenized the data to make it usable and expanded it with festival type and crew gender information. Cinando technical IDs and personal data have been anonymized. The data owner supports the publication of this data set.

Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”

A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org

Please cite this when using the dataset.

Detailed description of the dataset:

1 Film Dataset: Festival Programs

The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format.

The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data.

The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID.

The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data.

2 Survey Dataset

The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.

The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data.

The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.

The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset.

3 IMDb & Scripts

The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.

The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.

The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.

The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.

The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.

The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.

The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.

The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.

The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.

The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.

The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.

The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.

The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.

The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.

The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.

The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.

The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.

The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.

The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.

4 Festival Library Dataset

The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.

The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories,

Background What can we say about the success of a movie before it is released? Are there certain companies (Pixar?) that have found a consistent formula? Given that major films costing over $100 million to produce can still flop, this question is more important than ever to the industry. Film aficionados might have different interests. Can we predict which films will be highly rated, whether or not they are a commercial success?

This is a great place to start digging in to those questions, with data on the plot, cast, crew, budget, and revenues of several thousand films.

Data Source Transfer Summary We (Kaggle) have removed the original version of this dataset per a DMCA takedown request from IMDB. In order to minimize the impact, we're replacing it with a similar set of films and data fields from The Movie Database (TMDb) in accordance with their terms of use. The bad news is that kernels built on the old dataset will most likely no longer work.

The good news is that:

You can port your existing kernels over with a bit of editing. This kernel offers functions and examples for doing so. You can also find a general introduction to the new format here.

The new dataset contains full credits for both the cast and the crew, rather than just the first three actors.

Actor and actresses are now listed in the order they appear in the credits. It's unclear what ordering the original dataset used; for the movies I spot checked it didn't line up with either the credits order or IMDB's stars order.

The revenues appear to be more current. For example, IMDB's figures for Avatar seem to be from 2010 and understate the film's global revenues by over $2 billion.

Some of the movies that we weren't able to port over (a couple of hundred) were just bad entries. For example, this IMDB entry has basically no accurate information at all. It lists Star Wars Episode VII as a documentary.

Data Source Transfer Details Several of the new columns contain json. You can save a bit of time by porting the load data functions from this kernel.

Even in simple fields like runtime may not be consistent across versions. For example, previous dataset shows the duration for Avatar's extended cut while TMDB shows the time for the original version.

There's now a separate file containing the full credits for both the cast and crew.

All fields are filled out by users so don't expect them to agree on keywords, genres, ratings, or the like.

Your existing kernels will continue to render normally until they are re-run.

If you are curious about how this dataset was prepared, the code to access TMDb's API is posted here.

New columns:

homepage

id

original_title

overview

popularity

production_companies

production_countries

release_date

spoken_languages

status

tagline

vote_average

Lost columns:

actor_1_facebook_likes

actor_2_facebook_likes

actor_3_facebook_likes

aspect_ratio

cast_total_facebook_likes

color

content_rating

director_facebook_likes

facenumber_in_poster

movie_facebook_likes

movie_imdb_link

num_critic_for_reviews

num_user_for_reviews

Open Questions About the Data There are some things we haven't had a chance to confirm about the new dataset. If you have any insights, please let us know in the forums!

Are the budgets and revenues all in US dollars? Do they consistently show the global revenues?

This dataset hasn't yet gone through a data quality analysis. Can you find any obvious corrections? For example, in the IMDb version it was necessary to treat values of zero in the budget field as missing. Similar findings would be very helpful to your fellow Kagglers! (It's probably a good idea to keep treating zeros as missing, with the caveat that missing budgets much more likely to have been from small budget films in the first place).

Inspiration Can you categorize the films by type, such as animated or not? We don't have explicit labels for this, but it should be possible to build them from the crew's job titles.

How sharp is the divide between major film studios and the independents? Do those two groups fall naturally out of a clustering analysis or is something more complicated going on?

Acknowledgements This dataset was generated from The Movie Database 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.

Background

What can we say about the success of a movie before it is released? Are there certain companies (Pixar?) that have found a consistent formula? Given that major films costing over $100 million to produce can still flop, this question is more important than ever to the industry. Film aficionados might have different interests. Can we predict which films will be highly rated, whether or not they are a commercial success?

This is a great place to start digging in to those questions, with data on the plot, cast, crew, budget, and revenues of several thousand films.

Data Source Transfer Summary

We (Kaggle) have removed the original version of this dataset per a DMCA takedown request from IMDB. In order to minimize the impact, we're replacing it with a similar set of films and data fields from The Movie Database (TMDb) in accordance with their terms of use. The bad news is that kernels built on the old dataset will most likely no longer work.

The good news is that:

• You can port your existing kernels over with a bit of editing. This kernel offers functions and examples for doing so. You can also find a general introduction to the new format here.

• The new dataset contains full credits for both the cast and the crew, rather than just the first three actors.

• Actor and actresses are now listed in the order they appear in the credits. It's unclear what ordering the original dataset used; for the movies I spot checked it didn't line up with either the credits order or IMDB's stars order.

• The revenues appear to be more current. For example, IMDB's figures for Avatar seem to be from 2010 and understate the film's global revenues by over $2 billion.

• Some of the movies that we weren't able to port over (a couple of hundred) were just bad entries. For example, this IMDB entry has basically no accurate information at all. It lists Star Wars Episode VII as a documentary.

Data Source Transfer Details

• Several of the new columns contain json. You can save a bit of time by porting the load data functions from this kernel.

• Even in simple fields like runtime may not be consistent across versions. For example, previous dataset shows the duration for Avatar's extended cut while TMDB shows the time for the original version.

• There's now a separate file containing the full credits for both the cast and crew.

• All fields are filled out by users so don't expect them to agree on keywords, genres, ratings, or the like.

• Your existing kernels will continue to render normally until they are re-run.

• If you are curious about how this dataset was prepared, the code to access TMDb's API is posted here.

New columns:

• homepage

• id

• original_title

• overview

• popularity

• production_companies

• production_countries

• release_date

• spoken_languages

• status

• tagline

• vote_average

Lost columns:

• actor_1_facebook_likes

• actor_2_facebook_likes

• actor_3_facebook_likes

• aspect_ratio

• cast_total_facebook_likes

• color

• content_rating

• director_facebook_likes

• facenumber_in_poster

• movie_facebook_likes

• movie_imdb_link

• num_critic_for_reviews

• num_user_for_reviews

Open Questions About the Data

There are some things we haven't had a chance to confirm about the new dataset. If you have any insights, please let us know in the forums!

• Are the budgets and revenues all in US dollars? Do they consistently show the global revenues?

• This dataset hasn't yet gone through a data quality analysis. Can you find any obvious corrections? For example, in the IMDb version it was necessary to treat values of zero in the budget field as missing. Similar findings would be very helpful to your fellow Kagglers! (It's probably a good idea to keep treating zeros as missing, with the caveat that missing budgets much more likely to have been from small budget films in the first place).

Inspiration

• Can you categorize the films by type, such as animated or not? We don't have explicit labels for this, but it should be possible to build them from the crew's job titles.

• How sharp is the divide between major film studios and the independents? Do those two groups fall naturally out of a clustering analysis or is something more complicated going on?

Acknowledgements

This dataset was generated from The Movie Database API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additiona...

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of motion picture and video production (NAICS 512110), annual, for five years of data.

Global Paper Photographic Plate and Film Market Size Value by Country, 2023 Discover more data with ReportLinker!

Context

The cinema of India consists of films produced in the nation of India. Cinema is immensely popular in India. Every year more than 1800 films get produced in various languages in India. Mumbai, Chennai, Kolkata, Hyderabad, Kochi, Bangalore, Bhubaneshwar-Cuttack, and Guwahati are the major centers of film production in India. As of 2013, India ranked first in terms of annual film output, followed by Nigeria, Hollywood, and China. In 2012, India produced 1,602 feature films. The Indian film industry reached overall revenue of $1.86 billion (₹93 billion) in 2011. In 2015, India had a total box office gross of US$2.1 billion, the third-largest in the world. In 2011, Indian cinema sold over 3.5 billion tickets worldwide, 900,000 more than Hollywood.

The overall revenue of Indian cinema reached US$1.3 billion in 2000. The industry is segmented by language. The Hindi language film industry is known as Bollywood, the largest sector, representing 43% of box office revenue. The combined revenue of the Tamil and Telugu film industries represents 3 36%. Prominent movie industries include Tamil, Telugu, Malayalam, Kannada, and Tulu cinemas. Another prominent film culture is Bengali cinema, which was largely associated with the parallel cinema movement, in contrast to the masala films more prominent in Bollywood and Southern films at the time.

Indian cinema is a global enterprise. Its films have a following throughout Southern Asia and across Europe, North America, Asia, the Greater Middle East, Eastern Africa, China, and elsewhere, reaching over 90 countries. Biopics including Dangal became transnational blockbusters grossing over $300 million worldwide. Millions of Indians overseas watch Indian films, accounting for some 12% of revenues. Music rights alone account for 4–5% of net revenues.

Content

In this Dataset, we have 6750 Indian Actor (Male and Female) Images in 135 different categories or classes.

Structure of the Dataset

https://i.imgur.com/oJVXrdk.png" alt="">

Acknowledgment

This Dataset is created from Google Images: https://images.google.com/. If you want to learn more, you can visit the Website.

Cover Photo: https://images.google.com/

In 2024, Google ranked as the most valuable media and entertainment brand worldwide, with a brand value of 683 billion U.S. dollars. Facebook ranked second, valued at around 167 billion dollars. Part of the Tencent Group, WeChat and v.qq.com (Tencent Video) had a brand value of 56 billion and 17.5 billion dollars, respectively.

A Movies Dataset is a comprehensive collection of data related to films, often used for analysis, research, and building recommendation systems. This dataset typically includes a wide array of information about movies, providing detailed insights into various aspects of the film industry. Here’s

# ID: A unique identifier assigned to each movie in the dataset. This ID helps distinguish individual films and is useful when linking the movie to other associated datasets or information such as cast, crew, or reviews. # Title:

The official title of the movie. This field includes the name under which the film was released, and it might include original titles for international films.

# Overview: A brief summary or synopsis of the movie. This field provides an overview of the film's storyline, giving a concise description of the plot, themes, or major events without giving away major spoilers. Release Date:

The official date when the movie was first released to the public. This can be either a theatrical release date or a digital/streaming release date, depending on the distribution strategy.

# Popularity: A numerical score that represents the movie's overall popularity. This metric is typically based on factors such as viewership, audience interaction, and social media activity. It can fluctuate over time as new data becomes available.

# Vote Average: The average rating of the movie, calculated based on the votes or ratings submitted by users. This value represents how well the movie has been received by the audience, usually on a scale of 1 to 10.

# Vote Count: The total number of user votes or ratings submitted for the movie. This field provides insight into the level of audience engagement, showing how many people have rated the film. These columns collectively offer essential information for analyzing and comparing movies based on their release, audience reception, and popularity.

Global Paper Photographic Plate and Film Market Size Value Share by Country (US Dollars), 2023 Discover more data with ReportLinker!

George Lucas Filmography

George Lucas's Filmography Dataset

By Throwback Thursday [source]

About this dataset

Moreover, the dataset incorporates significant details regarding the production companies responsible for bringing these films to life. It meticulously notes both the production company behind each film and their involvement. Furthermore, it mentions the distributed company accountable for ensuring that these movies reached audiences through appropriate distribution channels.

In addition to this invaluable information about each film's production and release-related elements, this dataset also captures key personnel involved in their creation. It highlights notable figures such as directors who steered the creative vision of these movies and writers responsible for crafting engaging screenplays or stories. Furthermore, it identifies producers who oversaw various aspects of production.

Furthermore, an important aspect covered by this dataset is identifying those individuals or organizations that provided financial or creative support as executive producers for these films. Their contributions played a crucial role in shaping the artistic direction and feasibility of these projects.

How to use the dataset

How to use this dataset

• Film: This column includes the titles of the films in which George Lucas was involved. You can search for specific films or explore the entire filmography.

• Executive Producer: This column indicates the person or company who provided financial or creative support for each film. You can analyze how various executive producers contributed to different movies.

• Genre: The genre or category of each film is mentioned in this column. You can explore Lucas' works across different genres and see if there are any patterns or preferences.

• Director: The person who directed each film is mentioned here. You can examine George Lucas' collaborations with different directors and how their styles influenced his works.

• Writer: The individual responsible for writing the screenplay or story of a particular film is listed in this column. You can analyze Lucas' collaborations with different writers and explore their impact on storytelling within his movies.

• Production Company: This column specifies the company responsible for producing each film that George Lucas worked on. By studying these production companies, you can gain insights into industry partnerships and trends during specific time periods.

• Distributed By: The company that distributed each film is mentioned here. Analyzing distribution companies can provide information about marketing strategies, target audiences, and commercial success of these movies.

• Year: Each release year is listed in this column as a numeric value (e.g., 1977). You can analyze trends based on release years to understand how Lucas' career progressed over time and identify significant milestones.

9-11: Some columns are repeated twice in order to improve data consistency across multiple data sources (the original source used inconsistent formatting).

Please note that dates have been excluded from this guide as they are not provided in the dataset. However, you can utilize this information to explore relationships between release dates, running time, genres, and other variables present in the dataset.

Good luck exploring George Lucas' fascinating filmography!

Research Ideas

• Analyzing George Lucas's career: This dataset allows for a comprehensive analysis of George Lucas's filmography, including the films he directed, wrote, produced, and/or executive produced. Researchers or film enthusiasts can use this dataset to study his career progression or identify patterns in his work.
• Studying trends in film production: By examining the production companies and distribution companies associated with George Lucas's films, one can gain insights into industry trends and practices during different time periods. This dataset could be used to analyze the changing landscape of film production and distribution over the years.
• Genre analysis: The dataset provides information on the genre or category of each film in George Lucas's filmography. Researchers interested in analyzing popular genres across different decades or studying genre preferences can utilize this dataset to examine trends in filmmaking and audience preferences over time

Acknowledgements

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

License

See the dataset description for more informatio...

Success.ai’s Connected TV Data for Broadcast Media & Entertainment Professionals Worldwide offers a comprehensive dataset tailored for businesses seeking to engage with key decision-makers and innovators in the broadcast and entertainment industries. Covering professionals from global media corporations, production studios, streaming platforms, and ad-tech companies, this dataset provides verified contact numbers, email addresses, and geographic location data.

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Why Choose Success.ai’s Connected TV Data?

1. Verified Contact Data for Precision Targeting

   • Access verified phone numbers, work emails, and LinkedIn profiles of professionals in broadcast media, OTT platforms, and connected TV ecosystems.
   • AI-driven validation ensures 99% accuracy, minimizing wasted efforts and maximizing campaign efficiency.

2. Comprehensive Global Coverage

   • Includes profiles of professionals and companies from major entertainment hubs like Los Angeles, London, Mumbai, Seoul, and more.
   • Gain insights into regional content trends, technology adoption, and market opportunities across continents.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, business expansions, content strategies, and technology investments.
   • Stay aligned with fast-evolving industry trends and maintain relevance in your outreach efforts.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data for your campaigns.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, content creators, and tech innovators in broadcast media and entertainment worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic locations.
• Contact and Location Data: Gain direct access to verified phone numbers, email addresses, and physical office locations for strategic outreach.
• Leadership Profiles: Engage with CEOs, CTOs, production heads, and ad-tech professionals shaping connected TV strategies.

Key Features of the Dataset:

1. Decision-Maker Profiles in Media & Entertainment

   • Identify and connect with executives, content strategists, and technology leaders driving innovation in connected TV and broadcast media.
   • Target professionals responsible for ad placement, content distribution, and audience engagement.

2. Firmographic and Geographic Insights

   • Access detailed business information, including company hierarchies, geographic locations, and operational structures.
   • Pinpoint key players in regional markets and identify emerging content hubs.

3. Advanced Filters for Precision Campaigns

   • Filter companies by segment (streaming platforms, cable networks, production studios), geographic location, company size, or revenue range.
   • Tailor outreach to align with specific industry challenges, such as audience targeting, ad-tech integration, or content localization.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with media professionals.

Strategic Use Cases:

1. Ad-Tech and Marketing Solutions

   • Present innovative ad-placement technologies, audience analytics tools, or campaign management platforms to ad-buyers and media strategists.
   • Build relationships with professionals managing programmatic ad campaigns and audience segmentation strategies.

2. Content Distribution and Partnerships

   • Engage with streaming platforms, OTT services, and broadcast networks exploring partnerships for content distribution or technology integration.
   • Foster alliances to expand content reach, improve viewer engagement, or enhance monetization strategies.

3. Market Research and Consumer Trends

   • Analyze trends in content consumption, connected TV adoption, and audience preferences to refine your product or service offerings.
   • Leverage these insights to identify untapped opportunities and create competitive advantages.

4. Recruitment and Talent Solutions

   • Engage HR professionals and production leads seeking top talent for roles in media production, ad-tech development, or content marketing.
   • Offer workforce optimization solutions or recruitment services tailored to the media and entertainment industries.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality connected TV data at competitive prices, ensuring ...

Events Tickets Market Outlook

The global events tickets market size was valued at approximately USD 68.5 billion in 2023 and is projected to reach USD 110.3 billion by 2032, growing at a CAGR of 5.4% during the forecast period. This significant growth is driven by the increasing popularity of live events, advancements in digital ticketing platforms, and the rising disposable incomes of consumers worldwide.

The burgeoning growth of the events tickets market is primarily fueled by the relentless rise in live entertainment and sports events, which have become a vital part of social and cultural life. The proliferation of music festivals, concerts, theatrical performances, and sporting events has created a robust demand for event tickets. Additionally, the growing trend of experiential spending, where consumers prioritize spending on experiences over material goods, further propels the market. Technological advancements, particularly in mobile ticketing and blockchain technology, enhance the convenience and security of purchasing tickets, thus driving market growth.

Another significant growth factor is the increasing integration of advanced technologies such as artificial intelligence and machine learning into ticketing platforms. These technologies optimize customer experiences by providing personalized recommendations and dynamic pricing models. Furthermore, the implementation of augmented reality (AR) and virtual reality (VR) in events offers immersive experiences, thus attracting a broader audience and boosting ticket sales. The widespread adoption of mobile payments and digital wallets also facilitates seamless transactions, contributing to market expansion.

The shift of ticket sales from traditional offline methods to online platforms has revolutionized the events tickets market. Online ticketing platforms offer several advantages, including ease of access, a wide range of options, and secure payment gateways, which enhance user satisfaction. The convenience of purchasing tickets from anywhere at any time, coupled with the ability to compare prices and read reviews, has led to a substantial increase in online ticket sales. Moreover, social media marketing and influencer endorsements play a pivotal role in promoting events and driving ticket sales, particularly among younger demographics.

Live Entertainment Platforms have become a cornerstone in the events tickets market, transforming the way audiences engage with performances. These platforms provide a seamless interface for users to discover and access a wide array of live events, from concerts and theater productions to sports and festivals. By leveraging advanced technologies, live entertainment platforms offer personalized recommendations and real-time updates, enhancing the overall user experience. The integration of social media features allows users to share their experiences and connect with fellow enthusiasts, further amplifying the reach and popularity of events. As consumer preferences shift towards digital solutions, live entertainment platforms are poised to play a pivotal role in driving ticket sales and expanding market reach.

Regionally, North America holds a substantial share of the events tickets market, attributed to the high number of live events, robust digital infrastructure, and the presence of major market players. Europe follows closely, driven by a rich cultural heritage and a high disposable income. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing internet penetration, and a burgeoning middle class with a growing appetite for entertainment. Latin America and the Middle East & Africa regions are also anticipated to experience significant growth, supported by a rising number of events and improving economic conditions.

Type Analysis

The events tickets market is segmented by type into sports, concerts, theater, festivals, and others. Each segment caters to a unique audience and contributes differently to the overall market dynamics. Sports events dominate the market, driven by the global popularity of various sports such as football, basketball, and cricket. Major sports leagues and events like the FIFA World Cup, the Olympics, and the Super Bowl attract millions of spectators, both in-person and online, creating a substantial demand for tickets. Sponsorships, media rights, and merchandise sales further amplify the revenue generated from sports events.

In summary, this dataset provides a comprehensive record of Apple product appearances in various movies and TV shows, along with the frequency of their occurrences. Analyzing this dataset can offer valuable insights into Apple's strategic product placement endeavors and their marketing strategies within the entertainment industry. this dataset was scraped from the productplacementblog which claims is the best database in the product placement field over internet.

Note: Following the data scraping process, I incorporated additional information from the IMDb dataset available on Kaggle to enhance this dataset. This augmentation allowed me to determine the 'startYear,' 'averageRating,' and 'numVotes' columns. It is important to note that 'startYear' indicates the year of the title's initial release, not the precise release date. Furthermore, the 'imgCount' column represents the count of timestamps or scenes featuring Apple products. In cases where multiple Apple products appear within the same Movie/Show, we do not have specific scene-level granularity to discern which scenes correspond to each product. Therefore, 'imgCount' reflects the cumulative count of scenes where any Apple product was showcased

Content

• tconst: An alphanumeric unique identifier for each title.
• Title: The title of the movie or TV show.
• Movie / Show: A binary indicator (0 or 1) distinguishing between movies and TV shows.
• Season: Numeric representation of the season when the product appeared (in the case of TV shows).
• Episode: Numeric value representing the episode in which the product appeared (for TV shows).
• imgCount: The count of timestamps/scenes featuring Apple products.
• iPhone/iPad/iMac/MacBook/macOS/AirPods/Apple Watch: Eight boolean columns indicating which Apple products were featured.
• averageRating: IMDb's average rating of the title.
• numVotes: The number of IMDb votes the title received. -**startYear**: The year of the title's initial release.

The dataset comprises box office data and supplemental information for all theatrically released films adapted from Marvel Comics and DC Comics core superhero universes. TV specials and other projects that did not receive a wide theatrical release are not included.

Column explanation and suggested use:

Film: The title of the film in the U.S. market. Note that this may differ from other territories. For example, in the United Kingdom Avengers was retitled Avengers Assemble to distinguish it from the unrelated television series of the same name.

U.S. release date: The first day the film was available in theatres in the United States of America to the general public.

Box office gross Domestic (U.S. and Canada): The total gross earnings of the film in U.S. dollars in Hollywood’s domestic market (comprising the United States of America and Canada). Typically, the distributor for a motion picture receives slightly more than half of the final gross with the remainder going to the exhibitor (i.e. the cinema) across the theatrical window.

Box office gross Other territories: The total gross earnings of the film in U.S. dollars in Hollywood’s international market (comprising any countries the film released in except the USA and Canada). We might note that the split between distributors and exhibitors can be more variable than within the domestic market, but this is granular detail beyond the present analysis.

Box office gross Worldwide: The sum total gross earnings for all territories in U.S. dollars.

Budget: The production budget for the movie in U.S. dollars. This does not include any additional expenses relating to the movie beyond what it cost to make, most notably the marketing budget which can equal or even exceed the production budget on major motion pictures.

MCU: Indicates whether the film is part of the Marvel Cinematic Universe. Recorded as a Boolean value, with TRUE indicating that the film is part of the MCU. Note that some pre-existing films (such as 20th Century Fox X-Men films and Sony Pictures Spider-Man films) have been retroactively made part of the MCU’s multiverse (separate continuities that exist within a large continuity superstructure). This has mostly been driven by corporate acquisitions and mergers, but not entirely. There remain questions over the degree of interconnectedness for certain movies, particularly Sony’s films based on Spider-Man-related characters. To avoid confusion, this value is only given as TRUE when a film satisfies both the following criteria: (a) it was part of the MCU at the time of release (b) it is part of the “main” MCU timeline, elsewhere known as “The Sacred Timeline”.

Phase: States which phase of MCU this film belongs to (if not applicable NA is used). MCU phase was originally used as internal planning term at Marvel Studios, but has since become widely known and used by the general public. Noting the specific phases can be helpful in understanding public perception around the brand/franchise and public opinion on its health and reliability.

Distributor: The name of the film studio that distributed the movie within the United States. You may wish to note that several of the listed distributors are divisions of larger studios. Twentieth Century Fox was rebranded as 20th Century Studios following its acquisition by the Walt Disney Company. Columbia Pictures is a division of Sony Pictures Entertainment. New Line Cinema merged with what is now Warner Bros. Discovery in 2008 (then Time-Warner).

MPAA Rating: The age rating awarded under the Motion Picture Association film rating system. Note that age ratings can vary in other markets (as can the final cut of the film). The current available classifications from the MPA are:

G (General Audiences). All ages admitted. Nothing that would offend parents for viewing by children. PG (Parental Guidance Suggested). Some material may not be suitable for children. Parents urged to give “parental guidance”. May contain some material parent might not like for their young children. PG-13 (Parents Strongly Cautioned). Parents are urged to be cautious. Some material may be inappropriate for pre-teenagers. R (Restricted). Under 17 requires accompanying parent or adult guardian. Contains some adult material. Parents are urged to learn more about the film before taking their young children with them. NC-17 (No on 17 and under admitted). Clearly adult. Children are not admitted. This information can help with understanding the demographics of the audience, most notably by who would be excluded. It can also provide insight into the commercial viability of films given their relative MPAA rating. It may also help the user understand the relative maturity of a given film, though this is very much in terms of what the MPAA deems suitable for a given age rather than say the themes explored by th...

About Rotten Tomatoes

The movie rating system has become an integral part of the film industry, providing audiences with a benchmark to gauge the quality of a movie. Among the various rating systems, Rotten Tomatoes stands out as one of the most popular and influential platforms. Established in 1998 by Senh Duong, Rotten Tomatoes is a review aggregation website that collects and analyzes movie reviews from professional critics and audiences alike. The platform's popularity can be attributed to its unique approach to rating movies, which is based on the Tomatometer score. This score is calculated by tallying the number of positive reviews from top critics and weighing them against the total number of reviews. A movie with a Tomatometer score of 60% or higher is considered "fresh," while those with a score below 60% are labeled "rotten." This binary system provides a clear and concise way for audiences to determine whether a movie is worth watching or not.

About the Dataset

The extensive dataset includes a vast array of over 15,000 movies meticulously reviewed by Rotten Tomatoes. Within this rich repository, the thorough evaluations provided by previous viewers are meticulously documented, offering a fascinating glimpse into the collective perspectives on the diverse range of films. These reviews, encompassing a wide spectrum of opinions and critiques, are seamlessly integrated with comprehensive details about each movie, including intricate information about the cast members, gripping plot summaries, and other essential aspects that enrich the viewing experience. Through this well-curated compilation of reviews and movie specifics, the dataset not only provides a comprehensive overview of the extensive film collection but also highlights the nuanced interplay between audience reception and the artistic merit of each cinematic creation. In essence, the dataset represents a treasure trove of cinematic knowledge, capturing the essence of each movie through the lens of critical evaluation and informative analysis.

From the report page:

Since launching our weekly Top 10 and Most Popular lists in 2021, Netflix has provided more information about what people are watching than any other streamer except YouTube. And now we believe it’s time to go further.

Starting today we will publish What We Watched: A Netflix Engagement Report twice a year. This is a comprehensive report of what people watched on Netflix over a six month period1, including:

Hours viewed for every title — original and licensed — watched for over 50,000 hours2;

The premiere date3 for any Netflix TV series or film; and

Whether a title was available globally.

In total, this report covers more than 18,000 titles — representing 99% of all viewing on Netflix — and nearly 100 billion hours viewed.

Over 60% of Netflix titles released between January and June 2023 appeared on our weekly Top 10 lists. So while this report is broader in scope, the trends reflected in it are very similar to those in the Top 10 lists, including:

The strength of returning favorites like Ginny & Georgia, Alice in Borderland, The Marked Heart, Outer Banks, You, Queen Charlotte: A Bridgerton Story, XO Kitty and film sequels Murder Mystery 2 and Extraction 2;

The popularity of new series like The Night Agent, The Diplomat, Beef, The Glory, Alpha Males, FUBAR and Fake Profile, which generate huge audiences and fandoms;

The size of the audience of our films across every genre including The Mother, Luther: The Fallen Sun, You People, AKA, ¡Que viva México! and Hunger;

The enthusiasm for non-English stories, which generated 30% of all viewing;

The staying power of titles on Netflix, which extends well beyond their premieres. All Quiet on the Western Front, for example, debuted in October 2022 and generated 80M hours viewed between January and June; and

The demand for older, licensed titles, which generates tremendous value for our members and for rights holders.

When reading the report it’s important to remember:

Success on Netflix comes in all shapes and sizes, and is not determined by hours viewed alone. We have enormously successful movies and TV shows with both lower and higher hours viewed. It’s all about whether a movie or TV show thrilled its audience — and the size of that audience relative to the economics of the title; and

To compare between titles it’s best to use our weekly Top 10 and Most Popular lists, which take into account run times and premiere dates.

This is a big step forward for Netflix and our industry. We believe the viewing information in this report — combined with our weekly Top 10 and Most Popular lists — will give creators and our industry deeper insights into our audiences, and what resonates with them.

In 2021, the global sales revenue of YG Entertainment amounted to around 355.6 billion South Korean won. YG Entertainment is one of the biggest South Korean entertainment companies and manages major acts such as BIGBANG, WINNER, iKON, and BLACKPINK. The company also houses businesses focusing on cosmetics, character goods, and advertising.

The history of YG Entertainment

Yang Hyun-suk, a former member of the popular first-generation K-pop group Seo Taiji and Boys, founded YG Entertainment in 1996. With the debut of successful, musical acts such as Jinusean and 1TYM, YG Entertainment managed to establish itself as an early success story during the beginnings of the modern K-pop industry. Having kept up the momentum, it is generally counted among the most established and industry-leading K-pop entertainment companies .

The ongoing success of YG Entertainment

The company first began dabbling in the idol industry with the debut of BIGBANG in 2006, which would go on to become one of the most successful K-pop acts and boy bands worldwide. While the group was inactive between March 2018 and April 2022, their music videos are still among the most popular music videos from YG Entertainment artists . Currently, the company’s most prestigious active artist is the girl group BLACKPINK, which quickly became one of the most successful K-pop acts domestically and internationally after their 2016 debut. BLACKPINK and their solo acts have collectively sold around 21 percent of music albums released by female artists in South Korea in 2021 .