This project forecasts weekly sales for retail products based on historical sales, promotions, mobility trends, and special event data (Valentine's Day, Easter, Christmas).
- Exploratory Data Analysis (EDA)
- Feature Engineering (Lag, Rolling Averages, Promo Flags)
- Model Building with XGBoost
- Model Tuning and Evaluation
- Final Report (PDF Attached)
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- XGBoost Regressor
- Jupyter Notebook
- GitHub for version control
- Name: [Egbe Grace Egbe]
- LinkedIn: [https://www.linkedin.com/in/grace-egbe-77820b278/]