Let's take a quick detour to the Data Refinery tool. Data Refinery can quickly filter and mutate data, create quick visualizations, and do other data cleansing tasks from an easy to use user interface.
This section is broken up into the following steps:
Note: The lab instructions below assume you have a project already and have data you will refine. If not, follow the instructions in the pre-work and import data to project sections to create a project and assign data to your project.
Go the (☰) navigation menu and click on the
Projects link and then click on your analytics project.
Project home, under the
Assets tab, ensure the
Data assets section is expanded or click on the arrow to toggle it and open up the list of data assets.
Click the check box next to the merged data asset
XXXAPPLICANTFINANCIALPERSONALLOAN (the name of the file may vary,
XXX may be your username or the username of the person who granted you data access) to check it, then click the 3 vertical dots to the right, and select the
Refine option from the menu.
Data Refinery will launch and open to the
Data tab. It will also display the information panel with details of the data refinery flow and where the output of the flow will be placed. Go ahead and click the
X to the right of the
Information panel to close it.
We'll start out in the
Data tab where we wrangle, shape and refine our data. As you refine your data, IBM Data Refinery keeps track of the steps in your data flow. You can modify them and even select a step to return to a particular moment in your data’s transformation.
With Data Refinery, we can transform our data by directly entering operations in R syntax or interactively by selecting operations from the menu. For example, start typing
filter on the Command line and observe that the list of operations displayed will get updated. Click on the filter operation.
filter operation syntax will be displayed in the Command line. Clicking on the operation name within the Command line will give hints on the syntax and how to use the command. For instance, to filter for customers who have paid credits up to date, build the expression shown below. To inact the filter, you would
Apply the expression. For now, click Cancel to clear out the command line.
filter(`CreditHistory` == 'credits_paid_to_date')
We will use the UI to explore and transform the data. Click the
Let's use the
Filter operation to check some values. Click on
Filter in the left panel.
We want to make sure that there are no empty values in the
StreetAddress column. Select the
StreetAddress column from the
Column drop down list, select
Is empty from the
Operator drop down list, and then click the
Note: If there are records where the selected column is empty, they will be displayed after clicking the apply button. If there are no records for this filter, it means that the rows being sampled do not have any empty values for the selected column.
Now, click on the counter-clockwise "back" arrow to remove the filter. Alternately, we can also remove the filter by clicking the trash icon for the Filter step in the
Steps panel on the right.
We can remove these records with empty values. Click the
+Operation again and this time select the
Remove empty rows operation. Select the
StreetAddress column, then click the
Next button and finally the
Let's say we've decide that there are columns that we don't want to leave in our dataset ( maybe because they might not be usefule features in our Machine Learning model, or because we don't want to make those data attributes accessible to others, or any other reason). We'll remove the
For each columnn to be removed: Click the
+Operation button, then select the
Remove operation. Click the
Change column selection option.
Select column drop down, choose one of the columns to remove (i.e
FirstName). Click the
Next button and then the
Apply button. The columns will be removed. Repeat for each of the above columns.
At this point, you have a data transformation flow with 8 steps. As we saw in the last section, we keep track of each of the steps and we can even undo (or redo) an action using the circular arrows. To see the steps in the data flow that you have performed, click the
Steps button. The operations that you have performed on the data will be shown.
You can modify these steps in real time and save for future use.
Data Refinery allows you to run jobs at scheduled times, and save the output. In this way, you can regularly refine new data as it is updated.
Click on the "jobs" icon and then
Save and create job option from the menu.
Give the job a name and optional description. Note that you can
Edit the steps in this Data Refinery flow at this time. Also, note the output name, which in this case is USER1006.JRTAPPLICANTFINANCIALPERSONALLOAN_shaped.csv. Click the
Create and Run button.
The job will be listed as
Running and then the Status will change to
You can click
Edit next to Scheduled to run. Notice that you can toggle the Schedule to run switch and choose a date and time to run this transformation as a job or even change the compute environment for this transformation. We will not run this as a job, go ahead and click the
Go back to the top level of the data refinery view by clicking on the flow asset under the
'Associated Asset' section in the scheduled job page.
Clicking on the
Profile tab will bring up a view of several statistics and histograms for the attributes in your data.
You can get insight into the data from the views and statistics:
The median age of the applicants is 36, with the bulk under 49.
About as many people had credits_paid_to_date as prior_payments_delayed. Few had no_credits.
The median was 3 years for duration at current residence. Range was 1-6 years.
Let's do some visual exploration of our data using charts and graphs. Note that this is an exploratory phase and we're looking for insights in out data. We can accomplish this in Data Refinery interactively without coding.
Visualizations tab to bring up the page where you can select columns that you want to visualize. Add
LoanAmount as the first column and click
Add Column to add another column. Next add
LoanDuration and click Visualize. The system will pick a suggested plot for you based on your data and show more suggested plot types at the top.
Remember that we are most interested in knowing how these features impact a loan being at the risk. So, let's add the
Risk as a color on top of our current scatter plot. That should help us visually see if there's something of interest here. From the left, click the Color Map section and select Risk. Also, to see the full data, drag the right side of the data selector at the bottom all the way to the right, in order to show all the data inside your plot.
We notice that there are more purple on this plot towards the top right, than there is on the bottom left. This is a good start as it shows that there is probably a relationship between the riskiness of a loan and its duration and amount. It appears that the higher the amount and duration, the riskier the loan. Interesting, let's dig in further in how the loan duration could play into the riskiness of a loan.
Let's plot a histogram of the
LoanDuration to see if we can notice anything. First, select
Histogram from the
Chart Type (Note: Click the
Continue button to switch charts).
On the left, select
LoanDuration for the 'X-axis', select
Risk in the 'Split By' section, check the
Stacked option, uncheck the
Show kde curve toggle, uncheck the
Show distribution curve toggle. You should see a chart that looks like the following image.
It looks like the longer the duration the larger the blue bar (risky loan count) become and the smaller the purple bars (non risky loan count) become. That indicate loans with longer duration are in general more likely to be risky. However, we need more information.
We next explore if there is some insight in terms of the riskiness of a loan based on its duration when broken down by the loan purpose. To do so, let's create a Heat Map plot.
At the top of the page, in the
Chart Type section, open the arrows on the right, select
Heat Map, and click on the
Continue button in the subsequent 'Switch charts?' window.
Risk in the column section and
LoanPurpose for the
Row section. Additionally, to see the effects of the loan duration, select
Mean in the summary section, and select
LoanDuration in the
You can now see that the least risky loans are those taken out for purchasing a new car and they are on average 10 years long. To the left of that cell we see that loans taken out for the same purpose that average around 15 years for term length seem to be more risky. So one could conclude the longer the loan term is, the more likely it will be risky. In contrast, we can see that both risky and non-risky loans for the other category seem to have the same average term length, so one could conclude that there's little, if any, relationship between loan length and its riskiness for the loans of type other.
In general, for each row, the bigger the color difference between the right and left column, the more likely that loan duration plays a role for the riskiness of the loan category.
Now let's look into customizing our plot. Under the Actions panel, notice that you can perform tasks such as
Download chart details,
Download chart image, or set
Global visualization preferences (Note: Hover over the icons to see the names).
Click on the
gear icon in the
Actions panel. We see that we can do things in the
Global visualization preferences for
Notifications. Click on the
Theme tab and update the color scheme to
Dark. Then click the
Apply button, now the colors for all of our charts will reflect this. Play around with various Themes and find one that you like.
Finally, to save our plot as an image, click on the image icon on the top right, highlighted below, and then save the image.
We've seen a some of the capabilities of the Data Refinery. We saw how we can transform data using R code, as well as using various operations on the columns such as changing the data type, removing empty rows, or deleting the column altogether. We next saw that all the steps in our Data Flow are recorded, so we can remove steps, repeat them, or edit an individual step. We were able to quickly profile the data, to see histograms and statistics for each column. And finally we created more in-depth Visualizations, creating a scatter plot, histogram, and heatmap to explore the relationship between the riskiness of a loan and its duration, and purpose.