By Dr. Matthew A North
In Data Mining for the loads, professor Matt North—a former probability analyst and database developer for eBay.com—uses uncomplicated examples, transparent reasons and free, robust, easy-to-use software program to educate you the fundamentals of knowledge mining innovations which can assist you solution a few of your hardest enterprise questions.
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Extra info for Data Mining for the Masses
The data type is the kind of data an attribute holds, such as numeric, text or date. These can be changed in this screen, but for our purposes in Chapter 3, we will accept the defaults. Just below each attribute’s data type, RapidMiner also indicates a Role for each attribute to play. By default, all columns are imported simply with the role of ‘attribute’, however we can change these here if we know that one attribute is going to play a specific role in a data mining model that we will create.
Selecting the repository and setting a data set name for our imported CSV file. 40 Chapter 3: Data Preparation 15) We can now see that the data set is available for use in RapidMiner. To begin using it in a RapidMiner data mining process, simply drag the data set and drop it in the Main Process window, as has been done in Figure 3-20. Figure 3-20. Adding a data set to a process in RapidMiner. 16) Each rectangle in a process in RapidMiner is an operator. The Retrieve operator simply gets a data set and makes it available for use.
Results of changing missing data. 21) You can see now that the Online_Gaming attribute has been moved to the top of our list, and that there are zero missing values. Click on the Data View radio button, above and to the left hand side of the attribute list to see your data in a spreadsheet-type view. You will see that the Online_Gaming variable is now populated with only ‘Y’ and ‘N’ values. We have successfully replaced all missing values in that attribute. While in Data View, take note of how missing values are annotated in other variables, Online_Shopping for example.