This repository contains the supporting materials for a Store Sales Forecasting project. The project aims to predict future sales using machine learning models, specifically XGBoost and Random Forest.
Kaggle Dataset: Store Sales - Time Series Forecasting
The goal is to build regression models that can learn from past sales trends and generate accurate forecasts. Key steps include:
- Data loading and preprocessing
- Feature engineering (date-based, lag features, rolling statistics)
- Model training with XGBoost and Random Forest
- Model evaluation and visualization
Feel free to fork this repository, explore the data, and suggest improvements or further analyses.