Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking
This provides a dataset for researchers and practitioners working on machine learning applications in the Decentraland virtual economy, though it is incremental as it builds on existing data collection methods.
This paper introduces IITP-VDLand, a comprehensive dataset for Decentraland parcels with rich attributes including a rarity score, addressing data dispersion by collecting from multiple sources. Benchmarking over 20 state-of-the-art price prediction models on this dataset achieved a maximum R2 score of 0.8251 and 74.23% accuracy, with ensemble models outperforming others.
This paper presents a comprehensive Decentraland parcels dataset, called IITP-VDLand, sourced from diverse platforms such as Decentraland, OpenSea, Etherscan, Google BigQuery, and various Social Media Platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. Alongside, we introduce a key attribute in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world. Addressing the significant challenge posed by the dispersed nature of this data across various sources, we employ a systematic approach, utilizing both available APIs and custom scripts, to gather it. Subsequently, we meticulously curate and organize the information into four distinct fragments: (1) Characteristics, (2) OpenSea Trading History, (3) Ethereum Activity Transactions, and (4) Social Media. We envisage that this dataset would serve as a robust resource for training machine- and deep-learning models specifically designed to address real-world challenges within the domain of Decentraland parcels. The performance benchmarking of more than 20 state-of-the-art price prediction models on our dataset yields promising results, achieving a maximum R2 score of 0.8251 and an accuracy of 74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal that the ensemble models perform better than both deep learning and linear models for our dataset. We observe a significant impact of coordinates, geographical proximity, rarity score, and few other economic indicators on the prediction of parcel prices.