NFT Market Simulation Dataset for Fair Price Prediction and Auction Strategy Learning

The dataset was generated to support controlled experimental evaluation of blockchain-enabled NFT trading mechanisms combined with machine learning and reinforcement learning models. It was designed to simulate realistic NFT market conditions, including bid–ask dynamics, historical price variation, transaction volume, and participant reputation, in the absence of comprehensive publicly available NFT datasets. The data enabled systematic testing of fair price prediction using linear regression and random forest models, market participant segmentation via clustering, and adaptive bid adjustment strategies using Q-learning. All experiments were conducted under fixed parameter settings and random seeds to ensure full reproducibility and consistent comparison across pricing, clustering, and strategy optimization tasks.