The first 1/3 of this project will require the utilization of traditional machine learning methods on a dataset. The purpose of this portion of the project is promote the benefits of TDA. It is used to show data scientists that they can achieve very interesting results when using TDA. The second portion of this project will be incorporating TDA with machine learning. This portion of the project will be the most demanding due to lack of references. I will attempt to perform both a classification type project and prediction type project. Additionally, I may have to rely on HPCC to run my script (TDA is computationally expensive). The final portion of the project will be creating a Jupyter demo project for data scientists as well as a short semi-theoretical document that discusses the more important features of TDA.
I hope to create a script that is easy for those who are not familiar with TDA to follow. Ideally, I would like to create a document that scikit-tda is willing to publish. Hence, the document needs to be constructed in a manner that is easy to interpret by anyone who is new to TDA.
Thankfully, python is already has the libraries that I will need. They are as follows:
