applied-finance

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.

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.

Python-based implementation of financial or risk-related strategies developed as part of an academic applied project.

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

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

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

Applied finance notes

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

This is a comprehensive personal project on supervised machine learning. The goal is to create a machine learning model that decides whether a new credit card application should be approved.

I have built 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.

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

Optimizes stock portfolios using K-Means clustering, constrained optimization, and Monte Carlo simulation to maximize risk-adjusted returns.

I used 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.

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

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