This project was done during my time studying in the CS 340: Machine Learning course run by Dr. Mike Gelbart and co. Much of the base code can be attributed to him and his team. Implementations of the listed items were done by Matthew Hounslow.
In this repo you will find working implementations of decision stumps, trees, k-nearest neighbours, k-medians, random forests, random trees . All code is written in Python 3.6. Sklearn's classifiers were also used here.
- numpy
- Sklearn
- scipy
- matplotlib
In order to run the project, use python3 main.py -q <topic-number> where represents the section in main.py. Each section number pertains to a different technique in this case. More comments will be added to these files down the line to give greater clarity.