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.

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

Predict loan approvals using machine learning with SHAP explainability. Analyze customer data, build interpretable models, and visualize feature impact for business decision support.

Banking Churn Dashboard - Risk Segmentation & Retention KPIs using Excel, SQL & Power BI.

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

Enterprise ML system for banking customer churn prediction (91% accuracy). Delivers actionable retention insights with production-ready implementation including comprehensive testing and deployment options for real-world business impact.

EDA and visualization of banking loan applicant data to assess credit risk and support data-driven lending decisions.

Time series modelling and FTE planning based on loan application data from a Big 4 Australian bank

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.

This repository showcases a proof of concept of my work at Bartronics India Ltd containing Power BI dashboards and a custom SQL stored procedure developed for monitoring Banking Correspondent (BC) performance, transaction trends and rural banking KPIs in the Financial Inclusion System.

This project explores customer behavior using the Bank Marketing dataset to predict term deposit subscriptions. It includes EDA, feature engineering, model training, class imbalance handling, and evaluation using a logistic regression model.

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.

Comprehensive analysis of banking transaction data using Power BI to uncover customer behavior patterns