Machine learning project for predicting customer term deposit subscriptions

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.

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

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.

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

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.

EDA project analyzing customer behavior in bank marketing campaigns

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.

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.

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.

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

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

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 Excel Banking Analytics Dashboard (Risk, Transactions, Customers)

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