Tenant Credit Risk Scoring Model

Design a machine learning pipeline that develops a tenant credit risk scoring model using historical tenant data from a Google Sheet. Utilize pandas for data preprocessing, scikit-learn for feature selection and model training, implement multiple classification algorithms (Logistic Regression, Random Forest, XGBoost), and create a scoring system that predicts tenant default probability. Include model interpretability with SHAP values, cross-validation, and an automated reporting system that updates the source spreadsheet with risk classifications.