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.

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

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

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.

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

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

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.

Banking & Credit Analytics Dashboard: Analysis of 400M+ AZN loan portfolio using Power BI & AI (Key Influencers). Focused on interest rate optimization and branch performance.

End-to-end Canadian Credit Risk & PD modeling project using public Canadian lending data, ML models, SHAP explainability, Streamlit UI, and Power BI dashboard.

Analyzed bank loan application and repayment data using sql and power bi to evaluate approval trends, risk factors, and loan performance.

Predict loan approvals using machine learning with SHAP explainability. Analyze customer data, build interpretable models, and visualize feature impact for business decision support.

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

Credit card consumption prediction using Random Forest with RMSPE evaluation on banking dataset

Retail banking analysis covering customers, accounts, transactions, loans, cards, and service feedback using SQL, Python, and Power BI.

Banking portfolio risk dashboard | Power BI | DAX | Built on MySQL Financial Health Scoring System

Interactive Banking Loan Risk Dashboard in Excel with KPI analysis, customer segmentation, and default risk insights.

End-to-end Excel Banking Analytics Dashboard (Risk, Transactions, Customers)

End-to-end credit risk modeling and loan default prediction using LendingClub data

End-to-end churn prediction system using Python and machine learning, including data preprocessing, feature importance analysis, and business-driven insights.