YouTube Multiview Video Games Dataset from UC Irvine Machine Learning Repository

The University of California, Irvine (UCI), houses a remarkable collection of datasets, serving the machine learning community with invaluable resources. Among these is the YouTube Multiview Video Games Dataset, a goldmine for data enthusiasts seeking insights into the gaming world.

This dataset, which can be found on the UCI Machine Learning Repository website, contains approximately 120,000 instances, each described by 13 feature types, along with class information. It's specifically designed for exploring multiview topics like cotraining, ensembles, and clustering.

Located in Irvine, California, UCI provides researchers, students, and professionals with access to a diverse array of datasets for various research and educational purposes. Irvine itself is a vibrant city in Orange County, known for its proximity to beaches, parks, and cultural attractions.

https://archive.ics.uci.edu/dataset/269/youtube+multiview+video+games+dataset

You can download the necessary data at the above link

The YouTube Multiview Video Games Dataset offers a wealth of information for anyone interested in understanding the gaming landscape. With its extensive collection of instances and feature types, it provides a comprehensive view of video game-related content on YouTube.

Downloading the dataset is straightforward—all you need to do is visit the UCI Machine Learning Repository website and access the dataset through the provided link. It's freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

• Under the CC BY license, users are granted the right to copy, distribute, and transmit the work, as well as adapt it for commercial purposes, provided that proper attribution is given to the author or licensor.
• This license ensures that users can explore and utilize the dataset for various applications while respecting the rights of the creators and contributors.
• Moreover, the CC BY license does not affect users' fair dealing or fair use rights, moral rights of the author, or the rights of other individuals related to privacy or publicity.

The YouTube Multiview Video Games Dataset is a valuable resource for researchers, data scientists, and enthusiasts alike. Its wide range of instances and features allows for diverse analyses and insights into the world of video games as portrayed on YouTube.

Whether you're interested in understanding trends in gaming content, exploring user engagement with video game-related videos, or developing machine learning algorithms, this dataset offers endless possibilities for exploration and discovery.

By leveraging the YouTube Multiview Video Games Dataset, researchers and analysts can gain valuable insights that contribute to our understanding of gaming culture, content creation, and user behavior on one of the world's largest video-sharing platforms.

In conclusion, the YouTube Multiview Video Games Dataset from the UCI Machine Learning Repository is a valuable asset for anyone interested in delving into the world of gaming through data analysis. With its extensive collection of instances and features, it opens up exciting opportunities for research, exploration, and innovation in the field of machine learning and data science.

Key Takeaways from the YouTube Multiview Video Games Dataset:

• Comprehensive resource: The dataset contains roughly 120,000 instances, each with 13 feature types, offering a rich source of data for analyzing YouTube video game content.
• Multiview analysis: Designed for exploring multiview topics like cotraining, ensembles, and clustering, providing insights into various aspects of video game content on YouTube.
• Free and open access: The dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, allowing free download, distribution, adaptation, and even commercial use with proper attribution.
• Diverse applications: The dataset is valuable for researchers, data scientists, and enthusiasts interested in understanding gaming trends, user engagement, and developing machine learning algorithms related to video game content.
• Insights into gaming culture: Analyzing the dataset can shed light on gaming culture, content creation, and user behavior on YouTube, providing valuable insights into the world of online gaming.