Machine Learning Interpretability of a Supervised Learning-based Bitcoin Trading Strategy

Faculty Mentor/s:
Prof. Patrick Zoro, Financial Engineering

Students on the team:

Project Description:
This project’s goal is to resolve the black box issue in Machine Learning by using AI tools. This project is going to discover the interpretability of supervised learning that are based on bitcoin trading strategy models. Interpretability in supervised learning refers to the ability to understand and explain how a model makes its predictions. It is essential to ensure that the decisions made by the model align with human intuition and can be trusted. Students will employ a machine learning approach using historical bitcoin data to train a model that can predict future bitcoin prices to help traders make more informed decisions.

Month/Year Project Began: