Store-Item-demand-forecasting

1. 1Build an XGBoost model using historical sales data
2. 2Prepare and preprocess the data by handling missing values and encoding categorical variables
3. 3Split the data into training and testing sets to evaluate the model's performance
4. 4Tune the hyperparameters of the XGBoost model to optimize its accuracy
5. 5Use the trained model to predict the next 1-2 weeks of SKU-level order quantity

Data scientists and analysts can benefit from this technique to improve demand forecasting, while business stakeholders can use the insights to inform inventory management and optimization decisions

💡 XGBoost can be used for accurate demand forecasting at the SKU level

Forecast store item demand with XGBoost!