Weka is a suite of machine learning software for data mining tasks. Weka is used at universities throughout the world to teach data mining processes. It is very similar to much more expensive software and does a great job. Weka is the product of the University of Waikato in New Zealand and was first implemented in its modern form in 1997. It uses the GNU General Public License (GPL). The software is written in the Java™ language and contains a GUI for interacting with data files and producing visual results.
The link to download the WEKA application is: www.cs.waikato.ac.nz/ml/weka/
Please make sure that you have selected the correct system type to download WEKA. When you go to the link above, you will see the long list of different hardware systems and operating system types that you can select and download WEKA.
To find out which system you have:
- If you have a Laptop, go to the Control Panel on your laptop and click on the system, a window will open that contains information about your laptop. It will tell you if you’re running Windows, 64bit or 32bit for example.
- If you have Mac, go to the Apple icon on your Apple Menu on the top left corner of your screen, and click on the “About this MAC”, to see your system information.
Please make sure you download the correct version to your system, otherwise will not work. In case you have the wrong version downloaded, just uninstall and install again.
Please check out the following videos for information about downloading WEKA.
- View the following video for an introduction to Weka: https://www.youtube.com/watch?v=Exe4Dc8FmiM
- Weka Data Mining Tutorial for First Time & Beginners: https://www.youtube.com/watch?v=m7kpIBGEdkI
- View the following video for downloading, installing Weka and exploring data files (HINT: if you are asked to install Java please click “Install”): https://www.youtube.com/watch?v=nHm8otvMVTs
Once you finish viewing all the video tutorials and have successfully installed Weka, please open the data file “diabetes.arff” and classify the data using the decision tree algorithm (J48). Your report will include a combination of screenshots and written work. In your report, please include:
- A screenshot of Weka Explore when the file “diabetes.arff” is successfully loaded
- A screenshot of Weka Explore when the classification is completed
- After viewing the classification results, please explain the confusion matrix, e.g., what are the True Positive (TP) number, the True Negative (TN) number, the False Positive (FP) number, and the False Negative (FN) number?
- Please use “Visualize tree” to view the decision tree and include a screenshot of the tree in your report. (100 words)
- total 3-4 pages