My team and I participated in the Jane Street Kaggle competition, where we developed a machine learning pipeline to forecast market trends using high-frequency financial data. Together, we implemented techniques like feature engineering, memory optimization, and ensemble modeling (LightGBM and CatBoost) to enhance prediction accuracy and model performance. We also ensured reliability through cross-validation and feature importance analysis. This collaborative project allowed us to apply our collective skills in data preprocessing and financial forecasting to tackle real-world challenges effectively.