Decentralized Finance (DeFi), a rapidly evolving ecosystem of blockchain-based financial applications, has attracted substantial capital in recent years. Lending protocols, which provide deposit and loan services similar to traditional banking, are central to DeFi. However, the lack of credit scoring in these protocols creates several challenges. Without accurate risk assessment, lending protocols impose higher interest rates to offset potential losses, negatively affecting both borrowers and lenders. Furthermore, the absence of credit scoring reduces transparency and fairness, treating all borrowers equally regardless of their credit history, discouraging responsible financial behavior and hindering sustainable growth. This paper introduces credit scoring models for crypto wallets in DeFi. Our contributions include: (1) developing a comprehensive dataset with 14 features from over 250 000 crypto wallets; and (2) constructing four credit scoring models based on Stochastic Gradient Descent, Adam, Genetic, and Multilayer Perceptron algorithms. These findings offer valuable insights for improving DeFi lending protocols and mitigating risks in decentralized financial ecosystems.