Hissah AlMuneef | Published January 18, 2019
CloudData scienceMachine learningObject StoragePredictive analytics
Loans are the core business of loan companies. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.
In this tutorial, we’ll build a predictive model to predict if an applicant is able to repay the lending company or not. We will prepare the data using Jupyter Notebook and then build the model using SPSS Modeler.
After completing this tutorial, you’ll understand how to:
In order to complete this tutorial, you will need the following:
The overall time of reading and following this tutorial is approximately one hour.
The dataset is from Analytics Vidhya
The format of the data:
From Watson Studio main page, click on New project. Choose Complete to get the full functionalities. Once you enter your project name, click on Create.
Open Find and add data on the right-side panel, drag and drop the dataset (.csv file) from your computer to that area.
Find and add data
(+) New flow
Change Data Asset
Let’s look into the summary statistics of our data using the Data Audit node.
We can see that some columns have missing values. Let’s remove the rows that have null values using the Select node.
Drag and drop the Select node, connect it with the Data Asset node and right click on it and open the node.
Select discard mode and provide the below condition to remove rows with null values.
(@NULL(Gender) or @NULL(Married) or @NULL(Dependents) or @NULL(Self_Employed) or @NULL(LoanAmount) or @NULL(Loan_Amount_Term) or @NULL(Credit_History))
Now our data is clean, and we can proceed with building the model.
Double click the node or right click to open it.
Choose Configure Types to read the metadata.
The model predicts the loan eligibility of two classes (Either Y:Yes or N:No). Thus, the choice of algorithms fell into Bayesian networks since it’s known to give good results for predicting classification problems.
Splite Data into training and testing sets using the Partition node, from Field Operations palette.
Double click the Partition node to customize the partition size into 80:20, change the ratio in the Training Partition to 80 and Testing Partition to 20.
Drag and drop the Bayes Net node from the Modeling Palette.
Double click the node to change the settings. Check Use custom field roles to assign Loan_Status as the target and all the remaining attributes as input except Partition and Loan_ID. When you finish, click Save.
Use custom field roles
The analysis report shows we have achieved 82.3% accuracy on our test data set with this model. At the end, you can build more models within the same canvas until you get the result you want.
Right-click on the Bayes Net node and select Save branch as a model.
Enter a name for the model. A machine learning service should be added automatically if you already created one.
Click on Save.
In the Asset page under Watson Machine Learning models you can access your saved model, where you can deploy it later.
Watson Machine Learning models
In this tutorial, you learned how to create a complete predictive model, from importing the data, preparing the data, to training and saving the model. You also learned how to use SPSS Modeler and export the model to Watson Machine Learning models.
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