This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

Discovering Hidden Trends in Global Video Games Sales

Platforms, Genres, and Profitable Regions

By Andy Bramwell [source]

About this dataset

This dataset contains sales data for video games from all around the world, across different platforms, genres and regions. From the thought-provoking latest release of RPGs to the thrilling adventures of racing games, this database provides an insight into what constitutes as a hit game in today’s gaming industry. Armed with this data and analysis, future developers can better understand what types of gameplay and mechanics resonate more with players to create a new gaming experience. Through its comprehensive analysis on various game titles, genres and platforms this dataset displays detailed insights into how video games can achieve global success as well as providing a wonderful window into the ever-changing trends of gaming culture

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How to use the dataset

This dataset can be used to uncover hidden trends in Global Video Games Sales. To make the most of this data, it is important to understand the different columns and their respective values.

The 'Rank' column identifies each game's ranking according to its global sales (highest to lowest). This can help you identify which games are most popular globally. The 'Game Title' column contains the name of each video game, which allows you to easily discern one entry from another. The 'Platform' column lists the type of platform on which each game was released, e.g., PlayStation 4 or Xbox One, so that you can make comparisons between platforms as well as specific games for each platform. The 'Year' column provides an additional way of making year-on-year comparisons and tracking changes over time in global video game sales.
In addition, this dataset also contains metadata such as genre ('Genre'), publisher ('Publisher'), and review score ('Review') that add context when considering a particular title's performance in terms of global sales rankings. For example, it might be more compelling to compare two similar genres than two disparate ones when analyzing how successful a select set of titles have been at generating revenue in comparison with others released globally within that timeline. Lastly but no less important are the three variables dedicated exclusively for geographic breakdowns: North America ('North America'), Europe (Europe), Japan (Japan), Rest of World (Rest of World), and Global (Global). This allows us to see how certain regions contribute individually or collectively towards a given title's overall sales figures; by comparing these metrics regionally or collectively an interesting picture arises -- from which inferences about consumer preferences and supplier priorities emerge!

Overall this powerful dataset allows researchers and marketers alike a deep dive into market performance for those persistent questions about demand patterns across demographics around the world!

Research Ideas

• Analyzing the effects of genre and platform on a game's success - By comparing different genres and platforms, one can get a better understanding of what type of games have the highest sales in different regions across the globe. This could help developers decide which type of gaming content to create in order to maximize their profits.
• Tracking changes in global video games trends over time - This dataset could be used to analyze how various elements such as genre or platform affect success over various years, allowing developers an inside look into what kind of videos are being favored at any given moment across the world.
• Identifying highly successful games and their key elements- Developers could look at this data to find any common factors such as publisher or platform shared by successful titles to uncover characteristics that lead to a high rate-of-return when creating video games or other forms media entertainment

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: Video Games Sales.csv | Column name | Description | |:------------------|:------------------------------------------------------------| | Rank | The ranking of the game in terms of global sales. (Integer) | | Game Title | The title of the game. (String) | | Platform | The platform the game was released on. (String) ...

Includes 24 hour recall data that children were instructed to fill-out describing the previous day’s activities at baseline, weeks 2 and 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks). Includes accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks. Includes demographic data such as weight, height, gender, race, ethnicity, and birth year. Includes relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10. Includes questionnaire data regarding exercise self-efficacy using the Children’s Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG). Resources in this dataset:Resource Title: 24 Hour Recall Data. File Name: 24 hour recalldata.xlsxResource Description: Children were instructed to fill out questions describing the previous day's activities at baseline, week 2, and week 4 of the intervention, after the intervention (6 weeks), and after washout (10 weeks).Resource Title: Actigraph activity data. File Name: actigraph activity data.xlsxResource Description: Accelerometer data using an ActiGraph to assess usual physical and sedentary activity at baseline, 6 weeks, and 10 weeks.Resource Title: Liking Data. File Name: liking data.xlsxResource Description: Relative reinforcing value data showing how children rated how much they would want to perform both physical and sedentary activities on a scale of 1-10 at baseline, week 6, and week 10.Resource Title: Demographics. File Name: Demographics (Birthdate-Year).xlsxResource Description: Includes demographic data such as weight, height, gender, race, ethnicity, and year of birth.Resource Title: Questionnaires. File Name: questionnaires.xlsxResource Description: Questionnaire data regarding exercise self-efficacy using the Children's Self-Perceptions of Adequacy in and Predilection of Physical Activity Scale (CSAPPA), motivation for physical activity using the Behavioral Regulations in Exercise Questionnaire, 2nd edition (BREQ-2), motivation for active video games using modified questions from the BREQ-2 so that the question refers to motivation towards active video games rather than physical activity, motivation for sedentary video games using modified questions from the BREQ-2 so that the question refers to motivation towards sedentary video games behavior rather than physical activity, and physical activity-related parenting behaviors using The Activity Support Scale for Multiple Groups (ACTS-MG).

Global Video Game Sales and Reviews

Global Video Game Performance: Sales, Reviews, and Rankings

By Andy Bramwell [source]

About this dataset

The elements covered in this well-curated dataset include: The ranking of the game based on global sales under the column 'Rank'. This metric provides perspective on how popular or successful a particular game has been across countries in comparison to others during its time. Noting that video games' popularity could vary greatly from one geography to another due to factors like cultural nuances, gamer preferences, etc., regional sales have been marked separately for North America (North America), Europe (Europe), Japan (Japan) as well as for other parts of the World excluding these three regions under the column 'Rest of World'.

For easy identification among massive chunks of data, we've included each game's title (Game Title) along with additional categorization based on their genre (Genre). From action-packed adventures to strategic board-like scenarios or enchanted magic realms - classifications cover it all! In addition, detailed information about publishers can be found under 'Publisher', which grants insights about leading companies dominating market shares.

Further details expand into mentioning platforms such as PS4, Xbox, PC where these games can be played under 'Platform'. A unique attribute covered in this database is ‘Review’. Given that critique ratings play an influential role in engaging new players into trying out a particular video game or boosting existing user morale regarding their choice; this numeric representation ranging typically from 1-10 vividly captures public opinion about them.

Lastly, just for keeping tabs on ever-evolving gaming technology standards where newer versions often outshine predecessors irrespective of actual gameplay quality itself; having release years mentioned ('Year') proves beneficial for categorizing them chronologically. This helps correlate whether higher sales figures can sometimes merely be indicative of more people having access to necessary high-end gaming hardware during later periods.

In essence, this dataset titled ‘Video Games Sales.csv’ holds immense potential for informative deep-dives into the Video Game industry's trends and paradigms, forming a solid foundation for market research, academic purposes or personal projects

How to use the dataset

This dataset provides extensive information about various video game titles, their sales performance across multiple regions, publisher details and game reviews. Follow the steps outlined below to make the most out of this remarkable dataset!

1. Game Research & Evaluation:

With columns such as 'Game Title', 'Genre' and 'Review', you can research on particular games or genres that interest you. You can evaluate a game based on its review scores, delving into what makes a top-rated game.

2. Publisher Analysis:

The 'Publisher' column lets you track which publishers are behind the most successful games in terms of sales and reviews. This analysis could be useful for people interested in business trends in gaming industry or trying to identify potential innovative publishers.

3. Regional Market Trend Identification:

You can use data from columns like ‘North America’, ‘Europe’, ‘Japan’ and ‘Rest of World’ to study regional market trends for certain genres or platforms; it might enable one to recognize patterns over time or cultural preferences with regard to video games.

4. Global Sales Analysis:

Using the 'Global' column, you could observe which games have been globally successful, going beyond regional preferences by genre or platform.

5. Platform Insight:

The platform on which a particular game is available is another significant factor (e.g., PC, PS4, Xbox). By utilizing the data contained in this dataset regarding platforms, one may learn how platform choice impacts global sales as well as discern any correlation between preferred platform types among specific regions.

Remember that every statistical analysis begins with knowing your data - dive deep into each variable; explore patterns within variables before looking at correlations between different fields.

Don't forget - when engaged with comprehensive datasets like these - creativity is your only limit! Happy analyzing!

Research Ideas

• Trend Analysis: This dataset can be used to analyze the trends in video game preferences over the years based on genre, publisher, platform and region. It can provide interesting insights into how consumer tastes have evolved with time and which game genres are becoming more popular.
• Sales Forecasting: U...

General video gaming use among the U.S. population increased significantly during the COVID-19 pandemic. Between May and December 2020, U.S. teens aged 15 to 19 years spent an average of 112.8 daily minutes on playing games and using computers for leisure, up from 73.8 minutes per day in the corresponding period of 2019. In 2024, the daily time spent on such activities among this age group decreased to 78.6 minutes per day.

Video Games Sales Dataset

About Dataset

This Dataset provides up-to-date information on the sales performance and popularity of various video games worldwide. The data includes the name, platform, year of release, genre, publisher, and sales in North America, Europe, Japan, and other regions. It also features scores and ratings from both critics and users, including average critic score, number of critics reviewed, average user score, number of users reviewed, developer, and rating. This comprehensive and essential dataset offers valuable insights into the global video game market and is a must-have tool for gamers, industry professionals, and market researchers. by source

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Columns

Research Ideas / Data Use

• Market Analysis: The video game sales data can be used to analyze market trends and identify popular genres, platforms, and publishers. This can be useful for industry professionals to make informed decisions about game development and marketing strategies.
• Sales Forecasting: The sales data can be used to forecast future trends and predict the success of upcoming games.
• Consumer Insights: The data can be analyzed to gain insights into consumer preferences and buying habits, which can be used to tailor marketing strategies and improve customer satisfaction.
• Comparison of Competitors: The data can be used to compare the sales performance of competing video games and identify market leaders.
• Gaming Industry Performance: The data can be used to evaluate the overall performance of the gaming industry and track its growth over time.
• Gaming Popularity by Region: The data can be analyzed to determine which regions are the largest markets for video games and which genres are most popular in each region.
• Impact of Reviews: The data can be used to study the impact of critic and user reviews on sales and the relationship between scores and sales performance.
• Gaming Trends over Time: The data can be used to identify trends in the gaming industry over time and to track the evolution of the market.
• Gaming Demographics: The data can be used to analyze the demographic makeup of the gaming audience, including age, gender, and income.
• Impact of Gaming Industry on the Economy: The data can be used to evaluate the impact of the gaming industry on the economy and to assess its contribution to job creation and economic growth.

Acknowledgements

if this dataset was used in your work or studies, please credit the original source Please Credit ↑ ⠀⠀⠀

Cross-sectional survey of individuals aged 16–25 residing in Spain (n = 1,000), fielded in June 2025. The dataset captures demographics, gaming practices, exposure to gamblified mechanics (e.g., loot boxes, randomized rewards, time-limited offers), spending bands, emotional responses, and perceptions among non-players.The survey questionnaire (available as related material) consists of three sections: 1) sociodemographic data; 2) gamers (n=927); and 3) non-gamers (n=73). To distinguish between gamers and non-gamers, a filter question was placed at the end of section 1 (“Do you regularly play video games, mobile games, or digital games?”).The first section collects information on: sex/gender, municipality of residence, province/community of residence according to Nielsen area, size of municipality of residence, level of education completed, and current employment status.Block two collects information on estimated weekly hours of play, the device usually used, estimated monthly money spent on video games, and the two most played games. It also includes five-point Likert scale questions (disagree/agree) on issues related to random mechanisms, payments, or gaming habits.The third section repeats the same pattern of five-point Likert scale questions (disagree/agree) but from the perspective of non-video game players.

The Rea Games dataset provides valuable insights into the gaming industry, offering detailed data on game sales, player demographics, and user engagement. This dataset is designed for exploratory data analysis (EDA) to uncover patterns and trends within the gaming sector, helping analysts, game developers, and industry researchers better understand player behavior, revenue drivers, and popular game genres.

Dataset Overview:

Game Sales: Detailed information on sales figures for various games, allowing for revenue analysis and trend identification. Player Demographics: Data on player age, gender, and region, enabling segmentation and targeted analysis. User Engagement Metrics: Information on player engagement levels, such as playtime, frequency, and in-game purchases, providing insights into player retention and monetization strategies. Game Categories: Classification of games by genre, platform, and popularity to identify trends across different segments of the gaming market. Columns:

game_id: Unique identifier for each game in the dataset. game_title: Name of the game. genre: Game genre/category (e.g., action, adventure, simulation). platform: Platform on which the game is available (e.g., PC, console, mobile). release_date: Release date of the game. sales: Sales figures for each game, either in units or revenue. player_age: Age of the player engaging with the game. player_gender: Gender of the player (e.g., Male, Female). region: Geographic region where the game is popular or sold. playtime: Average playtime or engagement hours per player. in_game_purchases: Amount or count of in-game purchases made by players. Possible Use Cases:

Sales Trend Analysis: Track sales trends over time to identify popular games and profitable genres. Player Demographic Analysis: Segment players by age, gender, and location for targeted marketing. User Engagement Analysis: Explore player engagement metrics to improve retention and in-game purchases. Genre Popularity: Analyze which game genres are most popular across different platforms and demographics. This dataset is a valuable resource for data scientists, marketers, and game developers who want to make data-driven decisions in the gaming industry. By examining sales, demographics, and engagement metrics, users can gain insights to optimize game development, marketing strategies, and monetization efforts.

AIMS: cross validation of self-report scales to improve the self-test on gameninfo.nl (now using: VAT scale) by exploring new scales and drafting a new instrument from those scales. RECRUITMENT: online recruitment: game journalism, Facebook & game information site (gameninfo.nl). Recruitment in two countries: Dutch Belgian (Flemish) and The Netherlands. Dutch sample: 431 cases. Flemish (Belgian) sample: 312 cases. Together: 743 cases. TIME: Average participation time 20 minutes. Dropout: relatively ‘tough’ list due to repetition of multiple problematic gaming measures: we lose multiple cases 282, ending up with a sample of approximately 461 cases. CLEANING: Removing cases that fail one of the two attentiveness questions: final total sample of 430 cases. SCALES: Digital Games Motivation Scale; Video Game Addiction Test (2012); Self Determination Theory Basic Needs; Clinical Video Game Addiction Test 2.0 (DSM-5 coverage); Depressive Mood Scale; Global Kids Online: Online Safety; Global kids Online: Excessive internet use [gaming]; Life Satisfaction; Mental Health Inventory-5; Digital ambitions (career as streamer or progamer); ICD-Gaming Disorder; Open Science Def. Behavioral Addictions; Attentiveness (data quality check item); Demographics: Age (years); Demographics: Education level; Demographics: Gender; Device choice; Favorite game; Gametime per session; IGD Disorder Scale Lemmens: persistence; IGD Disorder Scale Lemmens: tolerance; Time played per weekday (hours); Time played per weekend day (hours); Other favorite games (3 max); Days on which you play (mon, tues, etc.)

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Data obtained using a program from the site vgchartz.com.

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"Founded in 2005 by Brett Walton, VGChartz (Video Game Charts) is a business intelligence and research firm and publisher of the VGChartz.com websites. As an industry research firm, VGChartz publishes video game hardware estimates every week and hosts an ever-expanding game database with over 55,000 titles listed, featuring up-to-date shipment information and legacy sales data. The VGChartz.com website provides consumers with a range of content from news and sales features, to reviews and articles, to social networking and a community forum." - from the site vgchartz.com.

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"Since the end of 2018 VGChartz no longer produces estimates for software sales. This is because the high digital market share for software was making it both more difficult to produce reliable retail estimates and also making those estimates increasingly unrepresentative of the wider performance of the games in question. As a result, on the software front we now only record official shipment/sales data, where such data is made available by developers and publishers. The legacy data remains on the site for those who are interested in browsing through it." - from the site vgchartz.com.

What can you do with the data set?

If you are new to data analytics, try answering the following questions: - in what year did the active growth in the number of video games produced begin? What year was the most successful from this point of view? What can you conclude if you look at the number of video games released by country? - on what day and month were the largest number of video games released? What could be the reason for this pattern? - is there a dependence of the number of copies sold on the ratings of critics or users? - which gaming platforms, publishers and developers are the most common (the largest number of video games have been released over time)? - which gaming platforms, publishers and developers have the largest number of video game copies sold (over all time, the total number of copies sold was the largest)?

If you have enough experience, try solving a regression problem. Train a model that can predict the number of copies sold of video games: - what signs can be used to prevent leakage of the target variable? - how do outliers affect the quality of the model? - which metric should be chosen to evaluate the model? - can adding new data improve the predictive ability of the model? - does the trained model have signs of heteroscedasticity of the residuals? How does this affect the predictive ability of the model? What can you do?

Field descriptions:

The data contains the following fields: 1. name – name of the video game. 2. date - release date of the video game. 3. platform - gaming platform (All – all gaming platforms, Series – all video game series). 4. publisher – publisher. 5. developers - developer. 6. shipped - the number of copies sent (relevant for records with the values All and Series in the platform field). 7. total - total number of copies sold (millions of copies). 8. america - number of copies sold in America (millions of copies). 9. europe - number of copies sold in Europe (millions of copies). 10. japan - number of copies sold in Japan (millions of copies). 11. other - other sales in the world. 12. vgc - rating VGChartz.com. 13. critic - critics' assessment. 14. user - user rating.

Found an error or inaccuracy in the data?

This dataset is the result of painstaking work. After collection and systematization, the data is checked for integrity and correctness. If you notice an error or inaccuracy in the data, or have a suggestion on how to improve the data set, please let me know.

You can look at working with data in my github repository.

Data Description - gender - birth year - gamer : if the respondent has played video games in the past few week - League of Legends : yes or no - Rocket League : yes or no - Valorant : yes or no - Fortnite : yes or no - Minecraft : yes or no - Genshin Impact : yes or no - The Sims : yes or no - game hours : less than 1 hour, 1-5 hours, 5-20 hours, 20-50 hours, more than 50 hours - romantic : single or in a relationship - go out : on a scale from 1 to 4 (1 = never going out, 4 = always going out)

Dataset collected through a questionnaire distributed on Discord groups.

Video Game Sales and Ratings

Global Video Game Sales, Ratings, and User Insights

By Sumit Kumar Shukla [source]

About this dataset

The Video Games Sales and Ratings Dataset provides an in-depth view into the dynamic world of video games, offering a comprehensive analysis of sales and ratings across diverse platforms and publishers. This dataset contains valuable facets of information that bring to light various insights about the video game industry over the years.

The dataset includes critical aspects such as the Name of each individual video game which was accounted for in this data aggregation process. The name captures the branded title under which a specific game is marketed and sold within the global market.

Additionally, key details on numbers relating to sales are included as well; such as Global Sales which refers to the total number of copies each individual game has sold on all indicated platforms worldwide- recorded in millions; NA_Sales representing accumulated sales figures from North America- also captured in millions; JP_Sales showing similarly compiled data specifically for Japan and EU_Sales for Europe respectively, both equally reflected in millions.

There is more insightful granularity embedded within our Dataset including Other_Sales, that tallies copies sold outside of specifically mentioned regions (North American, European & Japanese markets), expanding our insights into an even wider spectrum.

This Dataset not only shares hard figures on sales but also valuable opinions voiced by professional critics & users alike with Critic_Count & User_Count detailing how many individuals had reviewed any specific product with Critic Score being an averaged rating given by critics while User Score echoes sentiments from regular end-users or consumers who purchased these products - showcasing public opinion on these games.

Critical parameters defining characteristics essential related to gaming experiences like Genre detail distinctive aspects or themes around gameplay found within respective titles while Platform lists down where these titles were played specifically (between options like PC based or console front like PS4, Xbox etc.). The Publisher spotlights deserved attention onto those who took upon themselves to disseminate this creative work unto masses while Developer's name elucidates those daring visionaries who birthed unique experiences with their own hands through coding & design.

Last but not least, the Rating as per ESRB (Entertainment Software Rating Board) is included to give a sense of what demographic brackets/age each title was intended and marketed for - an essential aspect for parents and individuals mindful about content consumed within video games.

In short, this Video Games Sales and Ratings Dataset offers insights into the vast world of video gaming from various impactful perspectives serving as a valuable learning resource to anyone interested in gaining understanding or deploying data-driven strategies within any facet of this industry

How to use the dataset

Exploring the Dataset

The first step in analyzing this dataset is getting familiarized with its structure:

• Name: This attributes refers to the name of each video game included in the dataset.
• Platform: This denotes the platform(s) on which a particular game operates.
• Year_of_Release: The year when a particular game was launched is depicted by this attribute.
• Genre: It indicates what type of genre does a certain video game correspond to.
• Publisher & Developer: These fields detail out which company has published and developed every game respectively.
• NA_Sales, EU_Sales, JP_Sales & Other_Sales: These signify sales numbers from North America (NA), Europe(EU), Japan(JP) regions as well as other parts of world respectively (measured in millions).
• Global_Sales: This category refers to overall international sales for each described gaming product.
• Critic_Score & User_score: It represents average scores attributed by critics or users; where higher indicates better reception mostly measured on a scale often spanning 0–10 or 0–100.
• Critic_Count & User_Count: They denote how many critics/users have rated particular games respectively. -**Rating**: ESRB's categorization for games (e.g., E for Everyone,T for Teen,M for Mature etc.) is portrayed through it.

Data Analysis Recommendations

Here are some suggestions based on common practices:

• Sales Performance: You can examine sales figures categorized regionally or globally, leading to understanding which games have ...

The dataset provides valuable insights into the demographics and gaming habits of students, capturing various attributes that could be analyzed to uncover meaningful correlations. Each entry in the dataset includes the student's gender, identified as either "Female" or "Male," along with a unique school code that serves as an identifier for each institution. This allows for the categorization and grouping of students based on their respective schools, enabling comparative analyses across different educational institutions.

One of the key aspects covered in the dataset is the student's gaming experience, which includes the number of years they have been playing games. This attribute can indicate whether gaming is a long-term habit or a relatively new activity for the student. Additionally, the dataset records how frequently students engage in gaming, likely measured on a scale from 1 to 5, providing a quantitative representation of their gaming intensity. To further elaborate on gaming engagement, the dataset also tracks the average number of hours a student spends playing games daily. This metric can be crucial in understanding whether extended gaming sessions have an impact on academic performance. Moreover, the dataset distinguishes whether a student actively plays games or not, which can be particularly useful in comparative studies assessing the behaviors of gaming versus non-gaming students.

Beyond gaming habits, the dataset delves into socioeconomic factors by including the annual income of the student's family. This "Parent Revenue" variable can help researchers examine the potential influence of economic background on a student's gaming behavior and academic performance. Additionally, the education levels of both the student's father and mother are recorded, offering insights into whether parental education has any correlation with the student's gaming frequency, academic performance, or gaming choices.

Academic performance is another critical component of this dataset, represented by the "Grade" variable, which provides a measure of the student's academic standing. This information can be instrumental in investigating how gaming habits, parental background, and socioeconomic status contribute to or hinder academic success.

This dataset presents an excellent opportunity for analysis on platforms like Kaggle. Potential research directions could include exploring the relationship between gaming frequency and academic performance, investigating whether students from higher-income families spend more or fewer hours gaming, or analyzing if parental education has any impact on the types of games students play or their duration of play. By leveraging this dataset, researchers can identify trends and generate insights that may inform policies on gaming habits, parental involvement, and educational strategies.

In 2024, Call of Duty: Black Ops 6, published by Activision Blizzard, was the top-selling video game in the United States based on dollar sales. EA Sports College Football 25 was in second place, followed by the shooter Helldivers II. The dominance of AAA gaming productions In the video-game industry, AAA (pronounced Triple-A) is a classification that is used when describing or talking about video games that are produced and distributed by major or mid-tier video game developers. These game productions typically have a large development and marketing budget. When looking at the top-selling video games in the United States in 2023, the majority of the best-selling titles were produced by the biggest gaming companies worldwide. In addition to several publicly listed video game companies, gaming platform owners Sony and Nintendo also produce top-selling games with Sony's Marvel's Spider-Man 2 and Nintendo’s The Legend of Zelda: Tears of the Kingdom also reaching rank four and five, respectively. Spotlight: Call of Duty series Activision Blizzard’s long-running Call of Duty franchise usually features in many end-of-the-year lists as it is one of the top-grossing and best-selling gaming franchises worldwide. As of April 2021, the Call of Duty (CoD) series has generated more than 400 million lifetime unit sales and the most recent entry in the series is Call of Duty: Modern Warfare III, which was released in November 2023. The annual releases of the game series are rotated between several game development studios which are subsidiaries of Activision Blizzard: Infinity Ward, Sledgehammer Games, Treyarch, and Raven Software.

Goodwill Time Series for PlayAGS Inc. PlayAGS, Inc. designs and supplies gaming products and services for the gaming industry in the United States and internationally. It operates in three segments: Electronic Gaming Machines (EGM), Table Products, and Interactive Games (Interactive). The EGM segment offers a library of video and mechanical slot titles for the marketplace; EGM cabinets, including the Orion Starwall, Orion Curve Premium, Orion Rise, Big Red, Spectra UR43 Premium, Spectra SL49+ Premium, Spectra SL75+ with Premium and Core Content, Revel, Spectra UR49C, Spectra UR43, Orion Portrait, Orion Slant, Orion Curve, Orion Upright, ICON, and Spectra SL49+; and conversion kits that allow existing game titles to be converted to other game titles offered within that operating platform. This segment serves Class II Native American and Mexico gaming; and Class III Native American, commercial, and charitable jurisdictions. The Table Products segment provides table products, including live felt table games, side bets, progressives, card shufflers, signage, and other ancillary table game equipment; table technology related to blackjack, poker, baccarat, craps, and roulette; Dex S, a single deck card shuffler for poker tables; and the Pax S, a single-deck shuffler. This segment offers its products under the In Bet Gaming, Buster Blackjack, Double Draw Poker, and Criss Cross Poker. The Interactive segment provides a platform for business-to-business game aggregation used by real-money gaming; and business-to-consumer free-to-play social casino games through its mobile app, Lucky Play Casino. The company was formerly known as AP Gaming Holdco, Inc. and changed its name to PlayAGS, Inc. in December 2017. PlayAGS, Inc. was incorporated in 2005 and is headquartered in Las Vegas, Nevada.

Days-of-Inventory-On-Hand-Turnover Time Series for PlayAGS Inc. PlayAGS, Inc. designs and supplies gaming products and services for the gaming industry in the United States and internationally. It operates in three segments: Electronic Gaming Machines (EGM), Table Products, and Interactive Games (Interactive). The EGM segment offers a library of video and mechanical slot titles for the marketplace; EGM cabinets, including the Orion Starwall, Orion Curve Premium, Orion Rise, Big Red, Spectra UR43 Premium, Spectra SL49+ Premium, Spectra SL75+ with Premium and Core Content, Revel, Spectra UR49C, Spectra UR43, Orion Portrait, Orion Slant, Orion Curve, Orion Upright, ICON, and Spectra SL49+; and conversion kits that allow existing game titles to be converted to other game titles offered within that operating platform. This segment serves Class II Native American and Mexico gaming; and Class III Native American, commercial, and charitable jurisdictions. The Table Products segment provides table products, including live felt table games, side bets, progressives, card shufflers, signage, and other ancillary table game equipment; table technology related to blackjack, poker, baccarat, craps, and roulette; Dex S, a single deck card shuffler for poker tables; and the Pax S, a single-deck shuffler. This segment offers its products under the In Bet Gaming, Buster Blackjack, Double Draw Poker, and Criss Cross Poker. The Interactive segment provides a platform for business-to-business game aggregation used by real-money gaming; and business-to-consumer free-to-play social casino games through its mobile app, Lucky Play Casino. The company was formerly known as AP Gaming Holdco, Inc. and changed its name to PlayAGS, Inc. in December 2017. PlayAGS, Inc. was incorporated in 2005 and is headquartered in Las Vegas, Nevada.

Net-Interest-Income Time Series for PlayAGS Inc. PlayAGS, Inc. designs and supplies gaming products and services for the gaming industry in the United States and internationally. It operates in three segments: Electronic Gaming Machines (EGM), Table Products, and Interactive Games (Interactive). The EGM segment offers a library of video and mechanical slot titles for the marketplace; EGM cabinets, including the Orion Starwall, Orion Curve Premium, Orion Rise, Big Red, Spectra UR43 Premium, Spectra SL49+ Premium, Spectra SL75+ with Premium and Core Content, Revel, Spectra UR49C, Spectra UR43, Orion Portrait, Orion Slant, Orion Curve, Orion Upright, ICON, and Spectra SL49+; and conversion kits that allow existing game titles to be converted to other game titles offered within that operating platform. This segment serves Class II Native American and Mexico gaming; and Class III Native American, commercial, and charitable jurisdictions. The Table Products segment provides table products, including live felt table games, side bets, progressives, card shufflers, signage, and other ancillary table game equipment; table technology related to blackjack, poker, baccarat, craps, and roulette; Dex S, a single deck card shuffler for poker tables; and the Pax S, a single-deck shuffler. This segment offers its products under the In Bet Gaming, Buster Blackjack, Double Draw Poker, and Criss Cross Poker. The Interactive segment provides a platform for business-to-business game aggregation used by real-money gaming; and business-to-consumer free-to-play social casino games through its mobile app, Lucky Play Casino. The company was formerly known as AP Gaming Holdco, Inc. and changed its name to PlayAGS, Inc. in December 2017. PlayAGS, Inc. was incorporated in 2005 and is headquartered in Las Vegas, Nevada.

Here Is The Video Games Dataset That I Used For The Exploratory Data Analysis! :)

Data Description:

Rank: The ranking of the video game based on its global sales, with 1 being the highest-selling game.

Name: The title or name of the video game.

Platform: The gaming platform or console on which the game was released (e.g., Wii, NES, GB, DS).

Year: The year in which the video game was released.

Genre: The category or type of gameplay experience the game offers (e.g., Sports, Platform, Racing, Role-Playing, Puzzle, Misc, Shooter).

Publisher: The company responsible for publishing and distributing the video game.

NA_Sales: The total sales (in millions) of the video game in North America (NA).

EU_Sales: The total sales (in millions) of the video game in Europe (EU).

JP_Sales: The total sales (in millions) of the video game in Japan (JP).

Other_Sales: The total sales (in millions) of the video game in regions other than North America, Europe, and Japan.

Global_Sales: The total worldwide sales (in millions) of the video game, combining sales from all regions.

Digital Habits and Employment Status Survey

American Digital Usage and Employment Trends

By Joshua Shepherd [source]

About this dataset

This comprehensive dataset provides a rich and multi-faceted exploration into the intriguing world of digital habits, employment status, and demographics of Americans. Inspired by evolving modern lifestyle trends, this dataset meticulously draws information from varied topics such as gaming habits, job search techniques and broadband usage.

The first part of the dataset delves into the realm of video games and gaming culture. It explores various aspects related to individual's preferences towards different types of games across diverse platforms. It uncovers insights into how much time users spend on these games, their favoured genres and platforms (such as consoles or PC), along with their perspectives on important issues concerning violence in video games.

Next up is an insightful dataset that revolves around job seeking trends through digital channels. In a fast-paced business world where online resources have started playing an integral role in career progression and job hunt processes, this data provides valuable insights about Americans' reliance on internet services for finding potential jobs.

Hard-hitting questions revolving around workforce automation form yet another component of this extensive database. This section throws light upon the use of computers, robots or artificial intelligence to carry out tasks traditionally performed by human workers.

Probing further into modern relationship dynamics comes queries pertaining to online dating landscape. This segment explores Americans' attitudes towards online dating platforms - their usual go-to applications/web portals for seeking new relationships or love interests.

Lastly but importantly is an exhaustive set containing facts and figures regarding home broadband usage among Americans across all age groups & genders including their access to crucial cable TV services & smartphone possession rates & dependency levels over them in daily life activities ranging from shopping to banking & even learning new skills!

Collectively offering a well-rounded snapshot at contemporary American societies –this explorative data aims at providing stepping stones for researchers trying to understand these realms thereby serving larger cause making our society better

How to use the dataset

This dataset provides a rich collection of information about the digital habits, employment status, and secondary demographic data of respondents from the June-July 2015 Gaming, Job Search, and Broadband Usage Among Americans survey. With multiple sections regarding diverse topics such as gaming, online job searches, internet usage patterns and more fundamental demographics details - this dataset can be used for various kinds of exploratory data analysis (EDA), machine learning models or creating informative visualizations.

Here is how you can get started with this dataset:

1. Exploring Digital Habits:

The questions about video games ask if a respondent ever plays video games on a computer or console. This can be used to identify key trends in digital habits among different demographic groups - for instance correlation between age or gender and propensity towards gaming.

2. Analysing Job Searches:

The job seeking portion has information regarding use of internet in search processes and its effectiveness according to respondents’ opinion. You could perform an analysis on how working status (or even age group) affects the way individuals employ technology during their job searches.

3. Studying Broadband Usage:

Data about broadband usage at home would give insights into internet adoption rates among various demographic groups.

4.Predictive Modelling:

Potential predictive modeling could include predicting someone's employment status based on their digital habits or vice versa.

5.Cross-Referencing Data Points:

Using two or more datapoints can yield some interesting results as well - like finding out if gamers are more likely than non-gamers to frequently change jobs or seeing if there is any correlation with high speed broadband usage and employment type etc.

Before conducting any analysis do keep in mind that it would be beneficial to conduct some basic cleaning tasks such as checking for missing values, removing duplicates etc., suitable encoding discrete variables including education level into numerical ones based upon intuition behind categories ordinality could also provide better model performance.

This is just scratching the surface of p...

Net-Income-From-Continuing-Operations Time Series for PlayAGS Inc. PlayAGS, Inc. designs and supplies gaming products and services for the gaming industry in the United States and internationally. It operates in three segments: Electronic Gaming Machines (EGM), Table Products, and Interactive Games (Interactive). The EGM segment offers a library of video and mechanical slot titles for the marketplace; EGM cabinets, including the Orion Starwall, Orion Curve Premium, Orion Rise, Big Red, Spectra UR43 Premium, Spectra SL49+ Premium, Spectra SL75+ with Premium and Core Content, Revel, Spectra UR49C, Spectra UR43, Orion Portrait, Orion Slant, Orion Curve, Orion Upright, ICON, and Spectra SL49+; and conversion kits that allow existing game titles to be converted to other game titles offered within that operating platform. This segment serves Class II Native American and Mexico gaming; and Class III Native American, commercial, and charitable jurisdictions. The Table Products segment provides table products, including live felt table games, side bets, progressives, card shufflers, signage, and other ancillary table game equipment; table technology related to blackjack, poker, baccarat, craps, and roulette; Dex S, a single deck card shuffler for poker tables; and the Pax S, a single-deck shuffler. This segment offers its products under the In Bet Gaming, Buster Blackjack, Double Draw Poker, and Criss Cross Poker. The Interactive segment provides a platform for business-to-business game aggregation used by real-money gaming; and business-to-consumer free-to-play social casino games through its mobile app, Lucky Play Casino. The company was formerly known as AP Gaming Holdco, Inc. and changed its name to PlayAGS, Inc. in December 2017. PlayAGS, Inc. was incorporated in 2005 and is headquartered in Las Vegas, Nevada.