Shiney Dodti completed Data Analytics and AI

Built an end-to-end demand forecasting workflow on 9,648 historical transactions: resampled sales to monthly series, decomposed trend and seasonality, compared moving average, linear regression, and SARIMA forecasts, and evaluated them with MAE, RMSE, and MAPE on a true holdout period. Also produced regional, product, and channel EDA to support executive retail planning, with SARIMA emerging as the strongest model for peak-season forecasting.

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