Introduction

In this course, we have looked at four different approaches to classifying data:

• Fishers Linear Discriminant
• Linear Perceptron
• Adaline
• Linear Support Vector Machine

Additionally, we have used these approaches in assignments where the implementation was provided by some code written in Python.

Project Objective

In this project, your primary task is to apply 2 of these approaches to a larger scale data classification task.
In doing so, you will choose a single data set which you will classify with each approach.
The data set must have greater than a thousand labeled examples.
These examples must be classified into one of two classes.
Note that it is possible to find a large data set which has more than two classes from which you could always select examples from only two of the classes. If a dataset contains mixed data (numeric and other types) you can always simply ignore the non-numeric data.
Once the data set has been classified, your job is to compare the results from the classifiers that you chose. The comparison (in general – details will follow below) must include the following analyses:

• Computational Times for both training and testing
• A confusion matrix