Machine Learning Demand Forecasting
A machine learning project focused on predicting retail demand using historical sales data. The project applies XGBoost for forecasting and SHAP analysis to explain how different business variables influence prediction results.
About the Project
Machine Learning Demand Forecasting is a predictive analytics project developed to forecast retail demand using historical sales data. By leveraging the XGBoost algorithm, the model estimates future demand while identifying the key factors that influence prediction outcomes.
To improve model transparency, SHAP (SHapley Additive exPlanations) was applied to interpret feature importance, making the prediction results easier to understand and explain.
Project Objectives
The primary objective of this project was to develop a reliable demand forecasting model that supports data-driven decision making in retail. Beyond achieving accurate predictions, the project also focused on understanding how different business variables contribute to the forecasting results through explainable machine learning techniques.
Development Process
The project followed a structured machine learning workflow, starting from data preparation and preprocessing, followed by feature engineering, model training using XGBoost, performance evaluation, and result interpretation using SHAP analysis. This approach ensured that the model was both accurate and explainable.
Technologies Used
The project was implemented using Python in Google Colab and utilized several data science libraries to build, evaluate, and interpret the forecasting model.
Python
Google Colab
XGBoost
SHAP
Pandas
NumPy
Matplotlib
My Contribution
This was an individual project where I independently completed the entire machine learning workflow, including data preprocessing, feature engineering, model development using XGBoost, performance evaluation, and SHAP analysis to interpret the prediction results. I also documented the methodology and findings through a research report.
Project Outcome
The project successfully demonstrated how machine learning can be applied to forecast retail demand while providing transparent insights into the factors influencing prediction results. By combining predictive modeling with SHAP analysis, the project highlighted the value of explainable AI for business forecasting and data-driven planning.
What I Learned
Through this project, I gained practical experience in building end-to-end machine learning workflows, from data preprocessing and model development to evaluation and interpretation. It also enhanced my ability to communicate technical findings in a way that supports business understanding and informed decision-making.
