rhwang10 / Comparative-performance-analysis-of-Key-classification-algorithms-on-Microarray-Data

A study done on several machine learning algorithms to determine the algorithm with the best performance on several sets of clinical microarray data. All code is written in Python, and the SciKit.learn machine learning library and NumPy were used to analyze and output results. The final paper can be viewed in the PDF, titled the same as the repository name.

Algorithms.py

In this file, we run each classifier on each data set. There are 3 functions
, each runs a classifier on the passed in data set. Eaach function prints
out the evaluation metrics including a confusion matrix. The breast cancer
data file opened is in the /scratch/msong2 directory; however, the one submitted
is in the /home/msong2/cs68/Project-rhwang1-msong2/code/data directory. These
are the exact two files. 

Parse.py

This file contains all the functions for parsing. Each function parses a
particular data set. For example, the parse_mosquitoData function will
specifically parse the mosquito.csv file.