I will build and explain ml models with shap analysis

Do you have a dataset or machine learning project but need help understanding how the model actually works?

I provide research-oriented machine learning analysis with interpretable AI methods such as SHAP, feature importance analysis, and visual model explanation. My goal is not only to build models, but also to help you understand which variables drive predictions and how results can be interpreted in a meaningful way.

This gig is suitable for:

- Healthcare and medical AI projects

- Public health and epidemiology datasets

- Research and academic projects

- Classification and regression analysis

- XGBoost, Random Forest, Logistic Regression, and related ML workflows

- Researchers who need interpretable machine learning results

Services may include:

- Data preprocessing

- Machine learning model creation

- SHAP explainability analysis

- Feature importance interpretation

- ROC/AUC and model evaluation

- Visual reports and publication-style figures

- Research-friendly explanations and documentation

I mainly work with Python-based workflows and focus on interpretable machine learning rather than black-box predictions.