Consumer Banking Application

The data-set is related with direct marketing campaigns (were based on phone calls) of a banking institution. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The goal is to predict if the client will subscribe a term deposit

Retail Banking system is a microservice based web application which has been built using Java-8, Spring Boot, H2-Database and Angular 8. It has two types of end users, first the employee and second the customer. All the operations related to banking can be performed in this application.

Retail Banking Management website with Flask

The data-set is related with direct marketing campaigns (were based on phone calls) of a banking institution. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The goal is to predict if the client will subscribe a term deposit

This project builds a predictive model to evaluate the default risk of personal loans within retail banking. Leveraging an MLOps pipeline, it ensures efficient, scalable, and reliable model deployment on AWS, with a user interface created in Streamlit. The project incorporates experiment tracking, model tuning, and containerization.

A simple Retail Bank Management web-app built using Flask, SQLite