MachineLearningProject
Written in Python for COT4501
Download the Iris and Wine datasets available at the UCI Machine Learning repository in the platform of
your choice. Download two other datasets (separately chosen by each group). Describe the datasets in your
final project submission.
Execute a homegrown least-squares classifier on the 4 datasets with identical choices of training and test set
patterns. Document the mis-classification errors (and separate out each class errors as well) for both the the
least-squares classifiers. Document the settings of the free parameters for each dataset. Give a high level
summary of your findings based on your interpretation of the results. Use the rubric below (the one labeled
with “2” for more information.)