🛠️Feature Engineering and Dataset Preparation

• Feature Engineering: Extracts and constructs meaningful features from preprocessed data to help the AI models understand transaction patterns. Features include transaction frequency, average holding duration, transaction sizes relative to wallet balance, and interactions with known contracts (e.g., DeFi platforms, NFT marketplaces).

• Dataset Labeling: Employs a semi-supervised learning approach, where initially, a small subset of the data is manually categorized into different wallet types. This labeled dataset trains the initial models, which then iteratively label more data, subject to human review and correction.