Engineered a machine learning pipeline to predict electricity pricing trends utilizing advanced preprocessing techniques. • Optimized data quality by resolving missing records, executing feature scaling, minimizing dimensionality via Principal Component Analysis (PCA), and mitigating dataset skewness. • Trained an XGBoost regressor model, achieving an R2 score of 0.97. • Deployed system architecture utilizing a high-performance FastAPI backend paired with an interactive Streamlit frontend interface. [GitHub] | [Live Demo