According to the most recent data, U.S. viewers aged 15 years and older spent on average *** hours and ** minutes watching TV per day in 2024. Adults aged 75 and above spent the most time watching television at over **** hours, whilst 20 to 24-year-olds watched TV for less than *** hours each day. The dynamic TV landscape The way people consume video entertainment platforms has significantly changed in the past decade, with a forecast suggesting that the time spent watching traditional TV in the U.S. will probably decline in the years ahead, while digital video will gain in popularity. Younger age groups in particular tend to cut the cord and subscribe to video streaming services, such as Netflix, Hulu, and Amazon Prime Video. TV advertising in a transition period Similarly, the TV advertising market made a development away from traditional linear TV towards online media. While the ad spending on traditional TV in the U.S. generally increased until the end of the 2010s, this value is projected to decline to below ** billion U.S. dollars in the next few years. By contrast, investments in connected TV advertising are expected to steadily grow, despite the amount being just over half of the traditional TV ad spend by 2025.

The weekly time spent watching TV among Baby Boomers in the United States amounted to an average of ***** minutes (** hours and * minutes) in the first quarter of 2018. Boomers, or adults aged approximately 50 to 64 years old, are serious television fans, and 94 percent of adults in this age group watched television at least once per week.

Baby Boomers and television

According to data gathered in early 2019, adults aged between 50 and 64 years watched almost *** hours of live and time-shifted television per day, whereas those between ** and ** spent less than *** hours per day watching TV. Boomers tend to enjoy traditional formats more than younger consumers, though that is not to say that older adults do not make use of modern digital alternatives. Over ** percent of survey respondents aged 56 or above reported streaming or downloading TV series or movies each week, and some had even subscribed to a service purely to watch a specific show.

Long-Term-Debt Time Series for Sinclair Broadcast Group Inc. Sinclair, Inc., a media company, provides content on local television stations and digital platforms in the United States. It operates through two segments, Local Media and Tennis. The Local Media segment operates broadcast television stations, original networks, and content; provides free over-the-air programming and live local sporting events on its stations; distributes its content to multi-channel video programming distributors in exchange for contractual fees; and produces local and original news programs. This segment operates The Nest, a free over-the-air national broadcast TV network; Comet, a science fiction network; CHARGE!, an adventure and action-based network; TBD, a multiscreen TV network; The National News Desk, a news program; and Full Measure with Sharyl Attkisson, an investigative and political analysis program, as well as podcasts related to soccer and sports programming. Its Tennis segment offers Tennis Channel, a cable network that includes coverage of tennis' top tournaments and original professional sports, and tennis lifestyle shows; Tennis Channel International and Tennis Channel streaming services; T2 FAST, a 24-hours a day free ad-supported streaming television channel; Tennis.com; and FAST Channel Pickleballtv. The company also provides non-broadcast digital and internet solutions; and technical sales and services, including the design and manufacture of broadcast systems. In addition, it owns various investments in non-media related companies. The company distributes its content through its broadcast platform and third-party platforms that consist of programming provided by third-party networks and syndicators, local news, sports, and other original programming. Sinclair, Inc. was founded in 1971 and is headquartered in Hunt Valley, Maryland.

This dataset is collected by DataCluster Labs. To download the full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

The dataset is an extremely challenging set of over 1000+ original Television/TV images captured and crowdsourced from over 400+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size: 1000+ • Captured by: Over 1000+ crowdsource contributors • Resolution: 98% images HD and above (1920x1080 and above) • Location : Captured with 400+ cities across India • Diversity: Various lighting conditions like day, night, varied distances, viewpoints, etc. • Device used: Captured using mobile phones in 2022-2023 • Usage: Enhancing real-world applications such as smart home systems, retail analytics, and security monitoring.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

Data Cluster Labs exclusively own the images in this dataset and were not downloaded from the internet. A license can be purchased to access a larger portion of the training dataset for research and commercial purposes. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Total-Liabilities Time Series for Sinclair Broadcast Group Inc. Sinclair, Inc., a media company, provides content on local television stations and digital platforms in the United States. It operates through two segments, Local Media and Tennis. The Local Media segment operates broadcast television stations, original networks, and content; provides free over-the-air programming and live local sporting events on its stations; distributes its content to multi-channel video programming distributors in exchange for contractual fees; and produces local and original news programs. This segment operates The Nest, a free over-the-air national broadcast TV network; Comet, a science fiction network; CHARGE!, an adventure and action-based network; TBD, a multiscreen TV network; The National News Desk, a news program; and Full Measure with Sharyl Attkisson, an investigative and political analysis program, as well as podcasts related to soccer and sports programming. Its Tennis segment offers Tennis Channel, a cable network that includes coverage of tennis' top tournaments and original professional sports, and tennis lifestyle shows; Tennis Channel International and Tennis Channel streaming services; T2 FAST, a 24-hours a day free ad-supported streaming television channel; Tennis.com; and FAST Channel Pickleballtv. The company also provides non-broadcast digital and internet solutions; and technical sales and services, including the design and manufacture of broadcast systems. In addition, it owns various investments in non-media related companies. The company distributes its content through its broadcast platform and third-party platforms that consist of programming provided by third-party networks and syndicators, local news, sports, and other original programming. Sinclair, Inc. was founded in 1971 and is headquartered in Hunt Valley, Maryland.

About Dataset UPDATE: Source code used for collecting this data released here

Context YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for.

This dataset is a daily record of the top trending YouTube videos.

Note that this dataset is a structurally improved version of this dataset.

Content This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the US, GB, DE, CA, and FR regions (USA, Great Britain, Germany, Canada, and France, respectively), with up to 200 listed trending videos per day.

EDIT: Now includes data from RU, MX, KR, JP and IN regions (Russia, Mexico, South Korea, Japan and India respectively) over the same time period.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the five regions in the dataset.

For more information on specific columns in the dataset refer to the column metadata.

Acknowledgements This dataset was collected using the YouTube API.

Inspiration Possible uses for this dataset could include:

Sentiment analysis in a variety of forms Categorising YouTube videos based on their comments and statistics. Training ML algorithms like RNNs to generate their own YouTube comments. Analysing what factors affect how popular a YouTube video will be. Statistical analysis over time. For further inspiration, see the kernels on this dataset!

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

Exploring Daily Weather Report Usage

Understanding Regional and Socioeconomic Variations

By FiveThirtyEight [source]

About this dataset

This dataset contains survey responses from people about their daily weather report usage and weather check. It consists of columns such as Do You Typically Check a Daily Weather Report?, How do you Typically Check the Weather?, If You Had a Smartwatch (like the Soon to be Released Apple Watch), How Likely or Unlikely Would You Be to Check the Weather on That Device? Age, What is Your Gender?, and US Region. With this data, we can explore usage patterns in checking for daily weather reports across different regions, genders, ages and preferences for smartwatch devices in doing so. This dataset offers an interesting insight into our current attitudes towards checking for the weather with technology - and by understanding these patterns better, we can create more engaging experiences tailored to individuals’ needs

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started, it is helpful to first examine the columns in the dataset. The columns are Do you typically check a daily weather report?, How do you typically check the weather?, If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device?, Age, What is your gender?, US Region. Each row contains data for one survey participant, with their answers for each column included in each row.

The data can be used for exploring correlations between factors such as age, gender, region/location, daily weather checking habits/preferences etc.. Some of these variables are numerical (such as age) and others are categorical (such as gender). You can use this data when creating visualizations showing relationships between these factors. You may also want to create summary tables showing average values for different categories of each factor, allowing for easy comparison across groups or over time periods (depending on how much historical data is available).

Research Ideas

• Analyzing trends in the usage of daily weather reports by age, gender and region.
• Exploring consumer preferences for checking the weather via smartwatches and mobile devices in comparison to other methods (e.g., TV or radio).
• Examining correlations between people's likelihood to check their daily weather report and their demographic characteristics (location, age, gender)

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: weather-check.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------| | Do you typically check a daily weather report? | This column indicates whether or not the respondent typically checks a daily weather report. (Categorical) | | How do you typically check the weather? | This column indicates how the respondent typically checks the weather. (Categorical) | | If you had a smartwatch (like the soon to be released Apple Watch), how likely or unlikely would you be to check the weather on that device? | This column indicates how likely or unlikely the respondent would be to check the weather on a smartwatch. (Categorical) | | Age | This column indicates the age of the respondent. (Numerical) ...

Tax-Provision Time Series for Sinclair Broadcast Group Inc. Sinclair, Inc., a media company, provides content on local television stations and digital platforms in the United States. It operates through two segments, Local Media and Tennis. The Local Media segment operates broadcast television stations, original networks, and content; provides free over-the-air programming and live local sporting events on its stations; distributes its content to multi-channel video programming distributors in exchange for contractual fees; and produces local and original news programs. This segment operates The Nest, a free over-the-air national broadcast TV network; Comet, a science fiction network; CHARGE!, an adventure and action-based network; TBD, a multiscreen TV network; The National News Desk, a news program; and Full Measure with Sharyl Attkisson, an investigative and political analysis program, as well as podcasts related to soccer and sports programming. Its Tennis segment offers Tennis Channel, a cable network that includes coverage of tennis' top tournaments and original professional sports, and tennis lifestyle shows; Tennis Channel International and Tennis Channel streaming services; T2 FAST, a 24-hours a day free ad-supported streaming television channel; Tennis.com; and FAST Channel Pickleballtv. The company also provides non-broadcast digital and internet solutions; and technical sales and services, including the design and manufacture of broadcast systems. In addition, it owns various investments in non-media related companies. The company distributes its content through its broadcast platform and third-party platforms that consist of programming provided by third-party networks and syndicators, local news, sports, and other original programming. Sinclair, Inc. was founded in 1971 and is headquartered in Hunt Valley, Maryland.

Content

Puerto Rico Media

This folder contains data behind the stories: * The Media Really Has Neglected Puerto Rico * The Media Really Started Paying Attention To Puerto Rico When Trump Did

Online News Data

Data about Online News was collected using the Media Cloud dashboard, an open source suite of tools for analyzing a database of online news.

• mediacloud_hurricanes.csv contains the number of sentences per day that mention Hurricanes Harvey, Irma, Jose, and Maria in online news.
• mediacloud_states.csv (Updated through 10/10/2017) contains the number of sentences per day that mention Puerto Rico, Texas, and Florida in online news.
• mediacloud_trump.csv (Updated through 10/10/2017) contains the number of headlines that mention Puerto Rico, Texas, and Florida, as well as headlines that mention those three locations along with 'President' or 'Trump'.
• mediacloud_top_online_news.csv contains a list of sources included in Media Cloud's "U.S. Top Online News" collection.

TV News Data

TV News Data was collected from the Internet TV News Archive using the Television Explorer tool.

• tv_hurricanes.csv - contains the percent of sentences per day in TV News that mention Hurricanes Harvey, Irma, Jose, and Maria.
• tv_hurricanes_by_network.csv - contains the percent of sentences per day in TV News per network that mention Hurricanes Harvey, Irma, Jose, and Maria.
• tv_states.csv (Updated through 10/10/2017) - contains the percent of sentences per day in TV News that mention Puerto Rico, Texas, and Florida.

Google Search Queries

Google search data was collected using the Google Trends dashboard.

• google_trends.csv - Contains data on google trend searches for Hurricanes Harvey, Irma, Jose, and Maria.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Graph and download economic data for Nasdaq US Benchmark Radio and TV Broadcasters Index (NASDAQNQUSB40301035) from 2020-09-22 to 2025-11-07 about television, radio, broadcasting, NASDAQ, indexes, and USA.

Clash Royale TV Replays

Frame-by-frame gameplay recordings (~10 fps) from Clash Royale's TV Royale, covering all 31 arenas. Automated recording using tools from our github repository.

  Structure

arena_{XX}/{replay_uuid}/ ├── frames.parquet # Frame data └── preview.jpg # First frame thumbnail

Parquet Schema:

frame_id (int64): Frame number image (Image): PNG bytes hash (string): MD5 for deduplication

  Usage

from huggingface_hub import hf_hub_download… See the full description on the dataset page: https://huggingface.co/datasets/chrisrca/clash-royale-tv-replays.

Graph and download economic data for Nasdaq US Radio and TV Broadcasters Large Mid Cap Index (NASDAQNQUSB40301035LM) from 2020-09-22 to 2025-11-07 about television, radio, broadcasting, mid cap, market cap, NASDAQ, large, indexes, and USA.

This repository contains the data of the study "The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic".

Epidemiological data

The folder zika_USA_weekly_cases_2016.zip contains weekly ZIKV incidence counts reported by the US Centers for Disease Control and Prevention in 2016, by state. Data were extracted from reports made publicly available by the CDC at: https://zenodo.org/record/584136#.Xk07-RNKjOQ

Web news data

The file news_GDELT_data.csv.gz contains all news items extracted from the GDELT platform (https://www.gdeltproject.org/) matching TAX_DISEASE_ZIKA as a Theme, and United_States as a Location in the GDELT platform.

TV closed captions

The file zika_TV_mentions_dataframe.csv contains all the TV news items of 2016 matching the word ``Zika" in the TV News Archive https://archive.org/details/tv

Wikipedia pageview counts

Dataset 1: wikipedia_dataset1_zika_daily_pageview_usa.csv

Content of each line of the dataset: day, pageview_count

The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, from January 1st to December 31st, 2016.

Dataset 2: wikipedia_dataset2_zika_daily_pageview_bystate.zip

Content of each line of the dataset: day, pageview_count, state

The dataset contains the daily number of pageview counts of 128 different Wikipedia pages related to the Zika virus (aggregated and summed to total) originated in the United States, disaggregated by state, from January 1st to December 31st, 2016.

Dataset 3: wikipedia_dataset3_zika_pagecount_by_city.csv

Content of each line of the dataset: US_city, pageview_count_Zika,pageview_count_total

The dataset contains the total number of pageview counts of 128 different Wikipedia pages related to the Zika virus (pageview_count_Zika) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016.The dataset also contains the total number of pageview counts to all Wikipedia pages (all Wikipedia projects, pageview_count_total) originated in 788 cities (US_city) of the United States with a population larger than 40,000 in 2016."

United States E-Commerce Transactions: Value: Arts & Entertainment: TV Movies & Streaming data was reported at 86.903 USD in 10 May 2025. This records a decrease from the previous number of 235.549 USD for 09 May 2025. United States E-Commerce Transactions: Value: Arts & Entertainment: TV Movies & Streaming data is updated daily, averaging 20,728.810 USD from Dec 2018 (Median) to 10 May 2025, with 2321 observations. The data reached an all-time high of 293,574.520 USD in 02 Dec 2019 and a record low of 86.903 USD in 10 May 2025. United States E-Commerce Transactions: Value: Arts & Entertainment: TV Movies & Streaming data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s United States – Table US.GI.EC: E-Commerce Transactions: by Category.

Graph and download economic data for Nasdaq Small Cap Radio and TV Broadcasters Index (NASDAQNQUSS40301035) from 2020-09-22 to 2025-11-07 about television, radio, small cap, broadcasting, market cap, NASDAQ, indexes, and USA.

Graph and download economic data for Nasdaq Mid Cap Radio and TV Broadcasters NTR Index (NASDAQNQUSM40301035N) from 2020-09-22 to 2025-11-07 about television, radio, broadcasting, mid cap, market cap, NASDAQ, indexes, and USA.

Graph and download economic data for Nasdaq Large Cap Radio and TV Broadcasters TR Index (NASDAQNQUSL40301035T) from 2020-09-22 to 2023-09-15 about large cap, television, radio, broadcasting, market cap, NASDAQ, large, indexes, and USA.

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...

Upcoming 2020 Hollywood Movies

Calendar, Production Companies, Cast and Crew

By Priyanka Dobhal [source]

About this dataset

This dataset provides a comprehensive list of the top upcoming Hollywood movies of 2021. With detailed information about each movie, including titles, production companies, cast and crew members, and sources for further reference, viewers can stay up to date on what's playing in theaters throughout the year. Discover beloved classics and modern-day blockbusters that will transport viewers to new worlds and stories for hours of entertainment!

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To use this dataset correctly here are the steps you should follow: - Read through the columns of the dataset to understand what is included in them such as “Month”, “Day”, “Title” and other columns to become familiar with the data. - Have an idea about which feature of Hollywood movies that you would like to explore further such as finding movies by a certain actors or directors or producers or release dates etcetera.
- Filter out columns needed and manipulate them according your requirements prior analysing so it will be easier to focus on valuable insights providing columns only that relates to your purpose of exploring according movie features chosen previously (ex; filter out casting director name column if isn’t related). - Analyse each row in dataset required carefully since different rows can provide important pieces of clues regarding movie features selected (ex; month column tend to tell us when a movie is usually released).

5 Once all analysis has been done feel free utilize visuals so we can draw significance relationships more efficiently between different categorical/numerical variables using charts & graphs etcetera .

6 Finally make sure that collected information relate directly towards problem statement given by conducting thorough validations from obtained results from above steps giving reliable & correct available insights related feature chosen initially making sense in context subjective scenario at hand

Research Ideas

• Creating a timeline view of the up-coming Hollywood movie releases and their associated cast, crew and production company data.
• Using production company data to analyze what genres, actors, and directors are popular this year.
• Utilizing the cast and crew data to display the most experienced actor or filmmaker within each movie

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: WIki_Movies.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------------------------| | Month | The month in which the movie is scheduled to be released. (String) | | Day | The day of the month in which the movie is scheduled to be released. (Integer) | | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Ref | The source from which the data was collected. (String) |

File: Hollywood Movies - 2020.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------------| | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Opening | The date the movie is scheduled to be released in the US. (Date) | | Opening2 | The date the movie is scheduled to be released internationally. (Date) | | Ref. | The source from which the data was collected. (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 Priyanka Dobhal.