Proyek ML untuk segmentasi nasabah bank menggunakan K-Means Clustering dan prediksi segmen dengan model Klasifikasi. Fokus pada analisis perilaku untuk mendukung keputusan bisnis.
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.
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)
📊 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.
Banking & Credit Analytics Dashboard: Analysis of 400M+ AZN loan portfolio using Power BI & AI (Key Influencers). Focused on interest rate optimization and branch performance.
Predict loan approvals using machine learning with SHAP explainability. Analyze customer data, build interpretable models, and visualize feature impact for business decision support.
Retail banking analysis covering customers, accounts, transactions, loans, cards, and service feedback using SQL, Python, and Power BI.
End-to-end Canadian Credit Risk & PD modeling project using public Canadian lending data, ML models, SHAP explainability, Streamlit UI, and Power BI dashboard.
End-to-end credit risk modeling and loan default prediction using LendingClub data
End-to-end SQL and Power BI project analyzing bank loan performance, risk segmentation, and key performance indicators for business decision support.
Analyzed bank loan application and repayment data using sql and power bi to evaluate approval trends, risk factors, and loan performance.
End-to-end retail bank customer churn prediction with interpretable ML, class imbalance handling, and SHAP explainability.
EDA and visualization of banking loan applicant data to assess credit risk and support data-driven lending decisions.
End-to-end Data Warehousing and Business Intelligence solution for banking operations. Features comprehensive ETL pipelines using SSIS, Star Schema modeling in SQL Server, and OLAP Cube creation with SSAS.
"Predicting loan approval outcomes using machine learning models on applicant data to assist in risk-aware decision-making."
Loan Default Analysis - Multi-file joins, DateTime operations, String handling, DTI calculations
End-to-end bank loan performance analysis using SQL and Power BI, focusing on loan distribution, repayment trends, risk analysis, and key financial KPIs through interactive dashboards
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