This project involves predicting stock trade decisions (buy or sell) of U.S. House Representatives using a Random Forest Classifier. Key features such as trade amount, representative attributes, transaction dates, and political affiliations were analyzed. The model, optimized through GridSearch, achieved a test accuracy of approximately 65%. A fairness analysis was conducted to ensure unbiased predictions across political parties, leading to insights into trading patterns influenced by various factors.