Capstone project: employee engagement vs customer satisfaction vs branch performance (R, regression, clustering, Shiny)
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.
End-to-end bank customer churn prediction — EDA, feature engineering, Random Forest & Gradient Boosting models, interactive Streamlit app. Built with Python, Scikit-learn & Plotly.
📊 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.
End-to-end Canadian Credit Risk & PD modeling project using public Canadian lending data, ML models, SHAP explainability, Streamlit UI, and Power BI dashboard.
Banking & Credit Analytics Dashboard: Analysis of 400M+ AZN loan portfolio using Power BI & AI (Key Influencers). Focused on interest rate optimization and branch performance.
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."
Retail banking analysis covering customers, accounts, transactions, loans, cards, and service feedback using SQL, Python, and Power BI.
Interactive Banking Loan Risk Dashboard in Excel with KPI analysis, customer segmentation, and default risk insights.
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.
End-to-end credit risk modeling and loan default prediction using LendingClub data
End-to-end Excel Banking Analytics Dashboard (Risk, Transactions, Customers)
End-to-end data analytics project in the banking domain using Python, MySQL, and Power BI to generate business insights from raw transactional data.
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