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As a reminder , it is mandatory to have Java version higher than 1.6 installed in your machine. If you haven't, you can easily do this know by clicking on the following link

For KazAnova light (GUI), you may download the latest version in the following link. You can view the licence once you download it in a .txt file called "licence". This version is only available for Windows 32/64 but Mac and linux versions will follow later on. To learn more about the requirements and the sofwtare itself, you may go to the Software section . In case you are more interested about a tutorial you may try the Tutorial section which is dedicated to KazAnova light. It is possible that the download might stop at random points. If that happens, try to pause it and then resume. Else you can use the alternative download link via FileSwap . Press "SLOW DOWNLOAD" to download it for free. KazA Light 2.00 was released with tremendus performance in scorecard development plus some new features were added. This realease will soon be available for Mac and Linux. For the meantime you may download the standard versions for windows. The older version (1.00) is still available since the tutorials are written for this one.

For the Analytics' based java classes, I was quite suprized that it is more popular than the Gui! You may use the following link to download the Jar that contains both the compiled and the source (raw Java code) versions . You may attach it as an external Jar to your code. It is NOT mandatory (as it used to) anymore that you have Apache Commons Math in your code too, but it is good to have it anyway if you are doing something correlated with statistics or mathematics. The latest update of the KazAclasses made it certain that you do not need anything else to run the algorithms in this package so once you download it, you are good to go .The Javadoc is also provided along with a .zip file that contains very comprehensive examples about the most important of the provided classes. Generally do not be afraid to use the contact form or the email directly to contact and make suggestions for improvements-It will be good to know your opinion in general:

You may find the same examples here in a .txt format:

• Logistic Regression
• Discriminant Analysis
• Principal Components
• K-Means Clusterin
• Naive Bayes Classifier
• Multi-colinearity checks
• WoE and Information Value
• Multinomial Logistic Regression
• Linear Regression (OLS)