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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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 ↑ ⠀⠀⠀

In April 2025, total video games sales in the United States amounted to **** billion U.S. dollars, representing a one percent year-over-year increase. Generally speaking, the video game industry has its most important months in November and December, as video game software and hardware make very popular Christmas gifts. In December 2024, total U.S. video game sales surpassed **** billion U.S. dollars. Birth of the video game industry Although the largest regional market in terms of sales, as well as number of gamers, is Asia Pacific, the United States is also an important player within the global video games industry. In fact, many consider the United States as the birthplace of gaming as we know it today, fueled by the arcade game fever in the ’60s and the introduction of the first personal computers and home gaming consoles in the ‘70s. Furthermore, the children of those eras are the game developers and game players of today, the ones who have driven the movement for better software solutions, better graphics, better sound and more advanced interaction not only for video games, but also for computers and communication technologies of today. An ever-changing market However, the video game industry in the United States is not only growing, it is also changing in many ways. Due to increased internet accessibility and development of technologies, more and more players are switching from single-player console or PC video games towards multiplayer games, as well as social networking games and last, but not least, mobile games, which are gaining tremendous popularity around the world. This can be evidenced in the fact that mobile games accounted for ** percent of the revenue of the games market worldwide, ahead of both console games and downloaded or boxed PC games.

In 2024, the gaming sector experienced a significant number of layoffs because of post-COVID industry contraction which has led to studio consolidation and ultimately, an estimated 14,800 video gaming employees losing their jobs. Additionally, 2023 had also not been kind to the industry, as already 10,500 game developers lost their jobs during industry layoffs during the year.

Introduction

Welcome to the English-Chinese Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Chinese, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-Russian Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Russian, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-Swedish Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Swedish, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

DeepFake technology， born with the continuous maturation of deep learning techniques， primarily utilizes neural networks to create non-realistic faces. This method has enriched people’s lives as computer vision advances and deep learning technologies mature. It has revolutionized the film industry by generating astonishing visuals and reducing production costs. Similarly， in the gaming industry， it has facilitated the creation of smooth and realistic animation effects. However， the malicious use of image manipulation to spread false information poses significant risks to society， casting doubt on the authenticity of digital content in visual media. Forgery techniques encompass four main categories： face reenactment， face replacement， face editing， and face synthesis. Face editing， a commonly employed image manipulation method， involves falsifying facial features by modifying the information related to the five facial regions. As one of the commonly employed methods in facial editing， image inpainting technology involves utilizing known content from an image to fill in missing areas， aiming to restore the image in a way that aligns as closely as possible with human perception. In the context of facial forgery， image inpainting is primarily used for identity falsification， wherein facial features are altered to achieve the goal of replacing a face. The use of image inpainting for facial manipulation similarly introduces significant disruption to people’s lives. To support research on detection methods for such manipulations， this paper produced a large-scale dataset for face manipulation detection based on inpainting techniques. This paper specifically focuses on the field of image tampering detection， utilizing two classic datasets： the high-quality CelebA-HQ dataset， comprising 25 000 high-resolution （1 024 × 1 024 pixels） celebrity face images， and the low-quality FF++ dataset， consisting of 15 000 face images extracted from video frames. On the basis of the two datasets， facial feature regions （eyebrows， eyes， nose， mouth， and the entire facial area） are segmented using image segmentation methods. Corresponding mask images are created， and the segmented facial regions are directly obscured on the original image. Two deep neural network-based inpainting methods （image inpainting via conditional texture and structure dual generation （CTSDG） and recurrent feature reasoning for image inpainting （RFR）） along with a traditional inpainting method （struct completion（SC）） were employed. The deep neural network methods require the provision of mask images to indicate the areas for inpainting， while the traditional method could directly perform inpainting on segmented facial feature images. The facial regions were inpainted using these three methods， resulting in a large-scale dataset comprising 600 000 images. This extensive dataset incorporates diverse pre-processing techniques， various inpainting methods， and includes images with different qualities and inpainted facial regions. It serves as a valuable resource for training and testing in related detection tasks， offering a rich dataset for subsequent research in the field， and also establishes a meaningful benchmark dataset for future studies in the domain of face tampering detection.

Here are a few use cases for this project:

1. Sign Language Translator: The model can be used to create a sign language translator that can interpret sign language in real-time offering assistance to the deaf and hard of hearing individuals in communicating with others who are not familiar with sign language.

2. Educational Tools for Learning Sign Language: The model can serve as an educational tool for people who wish to learn sign language. With the help of this model, users can practice and correct their sign language gestures.

3. Accessibility solutions in public or private spaces: This model can be used in public environments like airports, museums, or schools to aid people who communicate via sign language. It would essentially act as an interpreter, converting sign language to digital text.

4. Video Conferencing: The model can be implemented into video conferencing platforms to enable real-time translations of sign language. This can ensure smoother and inclusive communication during online meetings or virtual events.

5. Gaming and Entertainment: The model can be utilized in the gaming industry, converting sign gestures into game controls. Similarly, it can be used in virtual reality environments to interact with objects or characters using sign language gestures.

Minecraft Statistics: The reports say that the gaming industry is expected to reach $431.87 billion by the year 2030. Since technological developments, not only there are laptops and PCs which are gaming-oriented but mobile devices have become compatible with many advanced games today. The recent release of the Harry Potter game ‘ Hogwarts Legacy is already doing its magic on the muggle world. These Minecraft Statistics include insights from various aspects that provide light on why Minecraft is one of the best games today. Editor’s Choice In Minecraft, 24 hours of the game is 20 minutes in real life. As of January 2023, the recorded number of players is 173.5 million. On average, 110,000 concurrent viewers are found on Twitch. Revenue generated from mobile downloads excluding in-game transactions counts for up to 41% of total Minecraft revenue. The Chinese edition of Minecraft has been downloaded more than 400 million times. To heal the players’ health healing potions have been used more than 1.1 billion times. Before launching Minecraft, the game was almost named a ‘Cave Game’. The game sometimes misspells its name by changing the order of words ‘C’ and ‘E’ with ‘Minecraft’. During the initial years of the pandemic, the database of total players increased by more than 14 million. The average age of a player is 24 years.

Introduction

Welcome to the English-Portuguese Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Portuguese, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Laptop Market Outlook

The global laptop market size is projected to grow significantly from USD 150 billion in 2023 to an estimated USD 230 billion by 2032, representing a compound annual growth rate (CAGR) of 4.9%. This growth is driven by a variety of factors including technological advancements, increasing demand for remote work solutions, and the continuous rise in e-learning. The proliferation of digital content, coupled with the need for portable computing devices, is further propelling the market. The shift towards more powerful, energy-efficient, and lightweight devices is also contributing to this upward trend. As consumers and businesses alike continue to value mobility without compromising on performance, the laptop market is poised for sustained growth over the forecast period.

One of the primary growth factors for the laptop market is the increase in remote work and distance learning opportunities. With the global shift towards remote working environments, particularly accelerated by the COVID-19 pandemic, there has been an unprecedented demand for portable and efficient computing devices. Laptops offer the flexibility needed for remote work, enabling users to access work-related resources from any location. The education sector has also witnessed a surge in demand as educational institutions have increasingly adopted digital learning platforms, necessitating the widespread use of laptops for students and educators. This trend is expected to continue as both the corporate world and educational institutions recognize the long-term benefits of flexible work and learning models.

Technological advancements in the laptop market are another critical growth driver. The development of high-performance processors, enhanced graphics capabilities, and longer battery life are setting new benchmarks in the industry. Manufacturers are focusing on innovation to meet the increasing expectations of consumers who are seeking devices that can handle more complex tasks. The advent of 5G technology and its integration into laptops is also expected to create new opportunities by enabling faster connectivity and improved performance. Additionally, the trend towards thinner and lighter laptops, such as ultrabooks, continues to gain traction, appealing to consumers who prioritize both portability and power.

The growth of the gaming industry is also significantly impacting the laptop market. Gaming laptops, which boast powerful processors and high-end graphics cards, are increasingly in demand. The rise of eSports and competitive gaming has fueled the need for devices that can deliver immersive gaming experiences. Moreover, the growing popularity of virtual reality (VR) and augmented reality (AR) has further driven the demand for high-performance laptops. As gaming becomes more mainstream and diverse, manufacturers are investing in developing specialized gaming laptops that cater to different segments of gamers, from casual players to serious enthusiasts.

Regionally, Asia Pacific is expected to exhibit the highest growth in the laptop market, driven by a burgeoning middle-class population and rapid digitalization. Countries like China and India are witnessing an increased adoption of laptops across various sectors, including education and business. North America and Europe remain key markets due to their technological infrastructure and high adoption rates of new technologies. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as potential growth areas, with increasing investments in technology and education sectors. As the global landscape evolves, regional dynamics will continue to play a significant role in shaping the future of the laptop market.

Product Type Analysis

The laptop market is broadly segmented into various product types, each catering to distinct consumer needs and preferences. Traditional laptops continue to dominate the market due to their versatility and affordability. These devices are favored by a wide range of consumers, from students to professionals, due to their well-balanced features that offer adequate performance for everyday tasks. Manufacturers have been focusing on enhancing the specifications of traditional laptops to include better processors, increased storage, and improved battery life, ensuring they remain competitive in a market with evolving consumer demands.

2-in-1 laptops, also known as convertible laptops, are gaining popularity due to their multifunctionality. These devices can switch between laptop and tablet modes, offering users the flexibility to use them for both work and entertainment purposes. The

Introduction

Welcome to the English-Dutch Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Dutch, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Introduction

Welcome to the English-Turkish Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Turkish, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

A global consumer survey conducted in March 2024 found that 18 percent of respondents were more likely to buy a video game if it was advertised as a collector or limited edition. However, 45 percent of respondents stated that they were not interested in limited edition releases.

Introduction

Welcome to the English-Finnish Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Finnish, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-French Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and French, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-Korean Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Korean, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-German Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and German, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application

Introduction

Welcome to the English-Arabic Bilingual Parallel Corpora dataset for the Gaming domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Arabic, providing a valuable resource for developing Gaming domain-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Gaming industry.

Format and Structure

Usage and Application