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

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.

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

No description provided.

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

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

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.

End-to-end data analytics project in the banking domain using Python, MySQL, and Power BI to generate business insights from raw transactional data.

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

Personal Data Analytics Portfolio showcasing Excel, SQL, and business analytics projects focused on banking risk, customer analytics, and financial 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.

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

End-to-End Credit Risk Analytics Dashboard using Power BI and Python (EDA, Correlation, Risk Modeling, Network Analysis)

End-to-end SQL and Power BI project analyzing bank loan performance, risk segmentation, and key performance indicators for business decision support.

🔍 Sistema de alerta temprana de Churn para Andes Bank. Análisis de causalidad mediante Python para identificar la fricción operativa como driver principal de abandono (99.5% de riesgo ante quejas). Incluye ETL, EDA bivariado y recomendaciones estratégicas de retención.

Machine learning–driven loan default risk prediction dashboard using XGBoost with transparent, case-specific credit risk explanations.

SQL case study analyzing bank loan approvals to uncover hidden risk patterns, decision bias, and the dominance of credit history using aggregations and window functions.

Rules based KYC Risk Scoring Dashboard -SQL and PowerBI. Automates customer classification into Low/Medium/High risk tiers using onboarding data.

Power BI project providing deep insights into UPI data. Features include data cleaning, interactive dashboards, analysis of transaction volumes (by week/day/month), geographic distribution, payment type breakdown, remaining balance by customer age, and key value matrices. Uncover trends and user behavior in the digital payments landscape.

Customer churn analysis for Horizon Bank identifying key drivers of customer attrition across France, Germany, and Spain to support data-driven retention strategies.

End-to-end retail bank customer churn prediction with interpretable ML, class imbalance handling, and SHAP explainability.

This project analyzes 284,000+ banking transactions to detect suspicious activity using time-series anomaly detection and an Agentic AI investigation workflow.