This video highlights the key observations and analysis from the machine learning code in my March Madness project. I walk through the exploratory data analysis, the 2 major components of the project (random forest model and logistic regression), and close off with key insights for future applications.
The corresponding code can be found here: https://github.com/mouctar-diarra/march-madness
This video highlights the key observations and analysis from the machine learning code in my Basketball Positions project. I walk through the exploratory data analysis, the performance of the machine learning model, and close off with applications for future use.
The corresponding code can be found here: https://github.com/mouctar-diarra/basketball-positions
This video is a deep dive into my March Madness project. I shed some light on the challenges I faced, lessons I learned, and explain some of the unique code powering my models.