• Analyzed a retail dataset (118k+ rows) and engineered features such as tax-adjusted price and seasonal time variables. • Applied Isolation Forest to detect anomalous price spikes/dips across years and product categories. • Trained regression and classification models; Random Forest achieved 83.13% price-tier accuracy. • Built a Power BI dashboard highlighting category trends, tax impact, and essential vs. non-essential price differences, with 15% faster insight discovery through interactive filters and drill-down visuals.