Credit scoring model for a financial company providing consumer loans to clients with limited credit history. The goal was to create an interpretable model to predict loan repayment likelihood and support loan approval decisions. Using historical loan data, financial details, and borrower behavior, I engineered new features and addressed data imbalance with techniques like SMOTE. Models such as Logistic Regression, Decision Trees, and Gradient Boosting were trained, with cost-sensitive learning applied to optimize the balance between false negatives and false positives. The final model achieved an AUC of 0.81, and SHAP values were used to ensure model interpretability, aiding customer relationship managers in making informed loan decisions. The project involved skills in data and predictive modeling, Scikit-learn, XGBoost, Random Forest, Docker, and Python.