The University of California, Irvine has a Machine Learning benchmark dataset collection available on the Web (see http://archive.ics.uci.edu/ml). Your objective is to choose the so-called Iris data set (see http://archive.ics.uci.edu/ml/datasets/Iris) from the available datasets, choose three data analysis methods i.e., logistic regression, neural nets (shallow), and decision trees, apply the three methods on the given dataset and compare the results of analysis by the three methods.

You are required to submit a brief report via Canvas on the assignment with the following structure:

I. Problem Statement and Description (about half a page)

II. Brief Description of the Three Methods Used (about half a page)

III. Experimental Results (about 1 page)

IV. Discussion of Results (about 1 page). You are free to use your own code or any other available code (at your own risk) for the implementation of the three methods. You do not need to submit any codes along with your report. This is an individual assignment. Discussions with the instructor/other students are allowed. However, your work must be your own and cannot be copy of others works.