Collection of Data science projects related to Applied Finance

I have built an automatic credit card approval predictor using machine learning techniques, just like the real banks do. This is a guided project under one of the courses that I took online.

Built a machine learning model to predict if a credit card application will get approved.

Build an ML-based credit card approval predictor for commercial banks to automate application analysis, saving time and reducing errors. High loan balances, low income, or excessive credit inquiries often lead to rejections. This project replicates real banks' automation for efficient, accurate, and faster decision-making.

Build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.

Build a machine learning model to predict if a credit card application will get approved

Applied finance notes

Analyze stock risk-return for investment decisions using Sharpe Ratio

Building a machine learning model to predict if a credit card application will get approved

Build a machine learning model to predict if a credit card application will get approved.

Commercial banks receive many applications for credit cards. Fortunately, this task can be automated with the power of machine learning, and virtually all commercial banks do it today. In this project, an automatic credit card approval predictor is built using machine learning techniques, just like real banks do.

A machine learning model to predict if a credit card application will get approved.

Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio

build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.

I used pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.

I have built a machine learning model to predict if a credit card application will get approved.

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.