Overview of this Lecture / week

This is the second week for Classification. I’m assuming you have completed the note, videos and lab exercises from last week. Don’t worry if you haven’t finished them, as you can continue working on them this week. But try to spend a bit of time catching up.

This week we will continue looking at some classification additional classification algorithms. These include, Naive Bayes, K-Nearest Neighbour, Support Vector Machines, Neural Networks and Regression. Don’t forget to look back over the notes and video from last week on how to evaluate classification problems, as this is very important.

I hope to give out the module assignment this week.


Click here to download the notes for the Classification topic. These are the same notes as last week.


Videos of Notes

Original xkcd Post

Lab Exercises

For the labs exercises this week you have 3 sets of notes to complete for Neural Networks, Regression and how to Assess & Evaluate Models

 1. Regression

 Click here to download the Regression Lab Exercises


 2. Neural Networks

 Click here to download the Neural Networks Lab Exercises


 3. Model Assessment

  Click here to download the Model Assessment Lab Exercises

Additional Reading Materials

24 Evaluation Metrics for Binary Classification (and when to use them)

How to load your own data sets into SAS OnDemand
SAS Guide for loading your own data using SAS Studio

Article by Thomas Davenport (Jan 06) on Competing on Analytics

Top 10 Data Mining Algorithms explained