akhil negi completed Data Analytics and AI

Built an end-to-end demand forecasting workflow on 9,648 historical transactions: performed sales, regional, and product EDA; decomposed monthly revenue to inspect trend and seasonality; and compared a naive moving-average baseline, linear regression, and SARIMA using a leakage-free train/test split. The final comparison showed SARIMA as the best model across MAE, RMSE, and MAPE, supporting more reliable peak-season planning.

• Leakage-Free Time-Series Splitting
• Comparative Forecast Evaluation
• Seasonality and Trend Analysis