Machine learning project for predicting customer term deposit subscriptions

📊 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.

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.

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.

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.

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.

EDA project analyzing customer behavior in bank marketing campaigns

📊 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.

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

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.

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

Interactive Power BI Dashboard analyzing 100,000+ banking records to monitor loan status, customer demographics, and financial KPIs.

AI-powered banking fraud analytics system using Python, Isolation Forest, Plotly, Gradio, and automated reporting.

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

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