BAN 440 Lab 1 - Data Preparation in RapidMiner (30 points)

Adapted from “Data Mining for the Masses” Chapter 3

Please follow the instructions carefully to finish lab assignment 1. In this assignment, you will be asked to make 8 screenshots and paste them to the “BAN440 Lab1 Submission YourLastName.docx” file. Once you are done with all 8 screenshots, please submit the word file “BAN440 Lab1 Submission YourLastName.docx” (with your own last name in the file name) to Canvas via the submission link.  

Note: “YourLastName” in this document refers to your own last name. Don’t literally type in “YourLastName.”

A. CREATE YOUR OWN REPOSITORY

1) Launch the RapidMiner application. This can be done by double clicking on your desktop icon named as “RapidMiner Studio” (as shown below), or by finding it in your application menu.

Within RapidMiner there are two main areas that hold useful tools: Repositories and Operators. The Repositories area is the place where you will connect to each data set. The Operators area is where all data mining tools are located. These are used to build models and otherwise manipulate data sets.

2) Follow the screenshot below to create your own new repository for BAN 440 class.

3) Click Next

Please change the Alias name to BAN440_YourLastName, and find a local folder where you want to put all the files related to this class. Then click Finish. (Important Note: you MUST name it as BAN440_YourLastName to get credit for this step.)

Hint: There is no specific requirement on where to save your repository for this lab. You may put it in your own FCB shared drive where you can access from any FCB computer. The repository you created here is computer specific, which means you may have to recreate it if you switch to a different computer.

4) You will see a newly created repository named as BAN440_YourLastName. 

B. DOWNLOAD AND IMPORT DATA

1) Please download the data file “Chapter03DataSet.csv” from Canvas and save it to your local drive. 

2) You can use Excel to view the downloaded file. This data set is very small, comprised of only 15 attributes and 11 observations. Our next step is to connect to this data set. When you browse this data set, you will notice there are some missing data as indicated by the green arrows (see below).

Missing data are data that do not exist in a data set. As you can see in the screenshot, missing data is not the same as zero or some other value. It is blank, and the value is unknown. Missing data are also sometimes known in the database world as null. Depending on your objective in data mining, you may choose to leave missing data as they are, or you may wish to replace missing data with some other value. We will deal with the missing data in later steps of this assignment.

At this point, we could do a number of complicated and technical things, such as connecting to a remote enterprise database. This, however, would likely be overwhelming for now. For the purposes of this lab assignment, we will only need to connect to comma separate values (CSV) files. Please be aware that in the real world, most data mining projects incorporate extremely large data sets, encompassing dozens of attributes and thousands or even millions of observations. We will use smaller data sets in this assignment, but the foundational concepts illustrated here are the same as for larger ones.

3) Click on the “Import Data” icon, as indicated in the red rectangle box on the picture below. Then click on “My Computer.” Note that by importing, you are bringing your data into a RapidMiner file, rather than working with data that are already stored elsewhere. If your data set is extremely large, it may take some time to import, and you should be mindful of disk space that is available to you.

4) Locate the file (Chapter03DataSet.csv), and then click on Next.

5) The column separation delimiter is Comma. Keep the default settings as shown in the screenshot below and click on Next.

6) RapidMiner will take its best guess at a data type for each attribute. The data type is the kind of data an attribute holds, such as polynominal, integer, or text.

7) Date types can be changed by following the screenshot below. Please change Gender from “polynominal” to “binominal.” 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 by clicking on “Change Role” if we know a particular attribute is going to play a specific role in a data mining model that we will create. Since roles can be set within RapidMiner’s main process window when building data mining models, we will just accept the default ‘attribute’ whenever we import data sets for our class. Also, you may note that “Exclude Column” allows you to not import some of the attributes if you don’t want to. Again, attributes can be excluded from models later in needed, so for this class, we will always include all attributes when importing data. Click on Next.

8) The final step for importing is to choose a repository to store the data set in, and to give the data set a name within RapidMiner. As shown in the following screenshot, please store the data set in the repository you just created, which is BAN440_YourLastName, and name it as Chapter03DataSet_YourLastName. Then click Finish. (Important Note: You MUST name it as Chapter03DataSet_YourLastName to get credit for this step).

9) Once you click on Finish, this data set will become available to you for any type of data mining process you would like to build upon it. The following screen shows you the Results Perspective.

C. RETRIEVE DATA OPERATOR

1) To continue, please click on “Design” tab on the top to switch back to Design Perspective. 

2) The following screenshot shows the Design view. We can see that the data set “Chapter03DataSet_YourLastName” is now available for use in RapidMiner.

3) To begin using it in a RapidMiner data mining process, simply drag the data set and drop it onto the Main Process window. 

4) Each rectangle in a process in RapidMiner is called an operator. The Retrieve operator simply gets a data set and makes it available for use. The small half-circles on the sides of the operator, and of the Main Process window, are called ports. In the following screenshot, an output (out) port from our data set’s Retrieve operator is connected to a result set (res) port via a spline. To draw the spline, please put your mouse cursor to the out port and then move your mouse while holding it, to connect to the res port (on the very left side of the Process window).

The splines, combined with the operators connected by them, constitute a data mining stream. To run a data mining stream and see the results, click on the blue, triangular Play button on the toolbar at the top of the RapidMiner window.

5) This will change your view from Design Perspective, which is the above screenshot where you can change your data mining stream, to Results Perspective, which shows your stream’s results, as pictured in the following screenshot.

6) When you hit the Play button, you may be prompted to save your process, and you are encouraged to do so. If not, please follow the screenshot below to “Save Process.”

7) Please save the process into the repository you just created, which is BAN440_YourLastName. Name your process as BAN440_Lab1_YourLastName. Then click OK. (Important Note: You MUST name it as BAN440_Lab1_YourLastName to get credit for this step).

8) You will then see the following screenshot. In the Result Perspective, you can find the repository we created, which is “BAN440_YourLastName” on the right side of the screen. You should also be able to see the dataset “Chapter03DataSet_YourLastName” and the process “BAN440_Lab1_YourLastName,” both under the “BAN440_YourLastName” Repository.

9) Please switch back to the Design Perspective by clicking on “Design” as shown below. You will find the repository “BAN440_YourLastName,” the dataset “Chapter03DataSet_YourLastName,” and the process “BAN440_Lab1_YourLastName” on the left side of the screen.

10) You can toggle between design and results perspectives by clicking on “Design” or “Result.”

D. REPLACE MISSING VALUES

1) In order to find a tool (or an operator) in the Operators area, you can navigate through the folder tree in the lower left-hand corner of the screen. RapidMiner offers many tools/operators and sometimes, finding the one you want can be tricky. There is a handy search box, indicated by the red rectangle in the screenshot below that allows you to type in key words to find tools/operators that might do what you need.

Type in the word ‘missing’ into this search box, and you will see that RapidMiner automatically searches for tools/operators containing this word in their names. We want to replace missing values, and we can see that there is an operator called Replace Missing Values.

2) Now, let’s add this operator to our stream. Please click and hold on the operator name (from the left-hand side Operators pane), and drag it up to your spline. When you point your mouse cursor on the spline, the spline will turn slightly bold, indicating that when you let go of your mouse button, the operator will be connected into the stream.

If you let go and the Replace Missing Values operator fails to connect into your stream, you can reconfigure your splines manually. Simply click on the out port in your Retrieve operator, and then click on the exa port on the Replace Missing Values operator. Exa stands for example set, and ‘examples’ is the word RapidMiner uses for observations in a data set. Be sure the exa port from the Replace Missing Values operator is connected to your result set (res) port so that when you run your process, you will have output. Your model should now look similar to the screenshot below.  

Please make a screenshot now and replace my screenshot #1 with yours in the submission file (named as “BAN440 Lab 1 Submission YourLastName.docx”). Please make sure your screenshot shows your own last name in the related items we have added so far (see the red box in the above screenshot.)

3) When an operator is selected in RapidMiner, it has an orange rectangle around it. This will also enable you to modify that operator’s parameters, or properties. The Parameters pane is located on the right side of the RapidMiner window (see below).   

4) For this assignment, we have decided to change all missing values in the Online_Gaming attribute to ‘N’, since this is the most common response in that attribute. To do this, please make sure the Replace Missing Values operator is selected (with the orange border), and then change the ‘attribute filter type’ to ‘single.’ Then, and you will see a dropdown box appears under it (for ‘attribute’), allowing you to choose the Online_Gaming attribute as the target for modification. Next, expand the ‘default’ dropdown box, and select ‘value’, which will cause a ‘replenishment value’ box to appear. Type the replacement value ‘N’ in this box. Note that you may need to expand your RapidMiner window, or use the vertical scroll bar on the left of the Parameters pane in order to see all options, as the options change based on what you have selected. When you are done, your parameters should look like below.  

5) Please note that there are many other options available to you in the parameters pane. We will not explore all of them here, but feel free to experiment with them. For example, instead of changing a single attribute at a time, you could change a subset of attributes in your data set. You will learn much about the flexibility and power of RapidMiner by trying out different tools and features. When you have your parameter set, click the Play button. This will run your process and switch you to Results perspective once again. Your results should look below.

Please make a screenshot now and replace screenshot #2 with yours in the submission file (named as “BAN440 Lab 1 Submission YourLastName.docx”). Please make sure to show the Online_Gaming attribute in your screenshot (see the red box), by scrolling to the very left.

As you can see, now the Online_Gaming attribute has been moved to the very left side of the attributes, and there are no missing values. All missing values for Online_Gaming have been replaced by “N.” Now, let’s look at the Online_Shopping attribute. A question mark (?) denotes a missing value in an observation. For this variable, suppose we do not wish to replace the null values with the mode, but rather, we wish to remove those observations from our data set prior to mining it. This can be accomplished through data reduction.