Machine learning-driven time-series product demand forecasting to optimize inventory and business planning.
| Model | R² Score |
|---|---|
| KNeighborsRegressor | 86.87 |
| RandomForestRegressor | 82.65 |
| XGBRegressor | 76.57 |
| RandomForestRegressor (with RandomizedSearchCV) | 82.98 |
This table presents the R² scores for different regression models tested on the dataset. The KNeighborsRegressor achieved the highest performance, followed by the optimized RandomForestRegressor.