An end-to-end ML application that predicts bank customer churn using 9 different models and provides AI-generated retention strategies with Groq LLM. Built with Streamlit for interactive predictions and visualizations.

Enterprise-style Credit Risk Analytics & Scorecard Modeling System using WOE, IV, Logistic Regression, XGBoost, KS, AUC, Credit Scoring, PSI & Drift Monitoring.

Machine learning project for predicting customer term deposit subscriptions

Fortune-500-grade banking analytics platform: OLTP -> medallion lakehouse -> Kimball star schema -> semantic layer -> 9-tab executive dashboard + 5 ML models (churn, fraud, segmentation, forecasting). Production-ready, governed, fully tested.

Business-oriented SQL and Power BI project analyzing customer behavior, deposits, loan performance, digital banking adoption, and risk analytics.

End-to-end analysis of bank loan default risk using historical lending data to identify key risk factors, assess borrower behavior, and support data-driven credit decisions.

SQL and Tableau project analyzing credit card customer segmentation, revenue concentration, and spending behavior.

📊 Banking Analytics Dashboard built with Power BI — exploring customer demographics, financial health, transaction behavior & card insights across 4 analytical pages with DAX-powered KPIs.

Explainable AI-powered credit risk scoring system with loan approval workflows, fairness monitoring, SHAP explainability, and interactive Streamlit dashboards for responsible financial risk analytics.

End-to-end bank customer churn prediction — EDA, feature engineering, Random Forest & Gradient Boosting models, interactive Streamlit app. Built with Python, Scikit-learn & Plotly.

Capstone project: employee engagement vs customer satisfaction vs branch performance (R, regression, clustering, Shiny)

Executive banking intelligence dashboard for analyzing customer conversions, campaign performance, and banking KPIs using Power BI, SQL, Python, and DAX.

End-to-end banking campaign analytics project using Power BI, SQL, Python, and statistical analysis to uncover customer behavior, campaign performance, engagement patterns, risk insights, and macroeconomic impact on subscription conversion.

End-to-End Credit Risk Analytics Dashboard using Power BI and Python (EDA, Correlation, Risk Modeling, Network Analysis)

EDA project analyzing customer behavior in bank marketing campaigns

Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

📊 Predict loan defaults reliably using a hybrid ensemble of machine learning models for enhanced accuracy and real-time insights in credit risk assessment.

Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

Proyek ML untuk segmentasi nasabah bank menggunakan K-Means Clustering dan prediksi segmen dengan model Klasifikasi. Fokus pada analisis perilaku untuk mendukung keputusan bisnis.

"Predicting loan approval outcomes using machine learning models on applicant data to assist in risk-aware decision-making."