This project employs Logistic Regression for binary classification, to predict whether a borrower is capable of repaying a loan based on various financial and demographic factors.
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Jan 19, 2026 - Jupyter Notebook
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shap-explainability
Here are 4 public repositories matching this topic...
End-to-end customer churn prediction project using the Telco dataset. Includes EDA, data preprocessing, Logistic Regression / Random Forest / XGBoost model comparison, SHAP explainability, and a production-ready prediction pipeline.
python machine-learning random-forest scikit-learn xgboost data-analysis logistic-regression customer-churn shap-explainability
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Jan 6, 2026 - Jupyter Notebook
FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.
data-science credit-card-fraud cybersecurity feature-engineering hyperparameter-tuning imbalanced-data smote fraud-detection classification-models optuna xgboost-classifier financial-security shap-explainability overfitting-analysis
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Mar 5, 2025 - Jupyter Notebook
I built this application to allow users to input various clinical parameters and receive an instant prediction of whether a patient is likely to have CKD. The app is hosted on Streamlit community cloud for public access.
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Aug 17, 2025 - Jupyter Notebook
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