IITP-VDLand: A Comprehensive Dataset on Decentraland Parcels

• URL: http://arxiv.org/abs/2404.07533v1
• Date: Thu, 11 Apr 2024 07:54:14 GMT
• Title: IITP-VDLand: A Comprehensive Dataset on Decentraland Parcels
• Authors: Ankit K. Bhagat, Dipika Jha, Raju Halder, Rajendra N. Paramanik, Chandra M. Kumar,
• Abstract summary: IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. We introduce a key in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world.
• Score: 1.83621951969607
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents IITP-VDLand, a comprehensive dataset of Decentraland parcels sourced from diverse 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 segments: (1) Characteristics Data-Fragment, (2) OpenSea Trading History Data-Fragment, (3) Ethereum Activity Transactions Data-Fragment, and (4) Social Media Data-Fragment. 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 performs 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.

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