Problem Statement
Sama financing company deals in home loans. They have presence across all urban and rural areas in Nigeria.
Customer can apply for home loan online or at their office and after that, the company manually validates the customer eligibility for loan.
The company wants to automate the loan eligibility process (real time) based on the customer detail provided while filling the application form online or physically on a paper. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others.
To automate this process, they have given a problem to identify the customers that are eligible for loan amount so that they can specifically target these customers.
Dataset
The company provided the following historical dataset for building a predictive modeling to automate the loan eligibility
Using Voyance OMNI platform to build a model
Now that we know what the problem is and we have the required data, we can go ahead to build a model to automate new loan submission.
Step 1
Create a project and upload historical data on Voyance OMNI platform
1.1
Upload historical data
Step 2
Select your model type and target variable
What is a target variable?
A target variable of a dataset is the feature of a dataset about which you want to gain a deeper understanding. In this case, its the loan_status
2.1
After selecting the model type and target variable, we can now name and create our model by clicking the “create model” button
Once that’s done, we can start training the model
Step 3
Once our machine learning model training is completed, we can now predict new customer loan request.
3.1
But first, we need to make the most performing algorithm (with best accuracy) active for our new prediction.
3.2
After doing that, we can predict new customer data via web or API. But for this tutorial, we’re predicting via Web.
After selecting new customer data, we need to name the prediction and run our prediction.
Prediction
In this image, we can see the newly created columns: Prediction, prob_Y and proba_N
Definitions
Y = Yes, Sama can loan this customer the requested loan amount
N = No, sama shouldn’t loan this customer the requested amount
As you’ve witnessed, we just created a loan eligibility machine learning model in less than 10mins.
Setup something similar for your business
To get started, visit https://app.voyancehq.com/request-access or reach out to Voyance team at https://voyancehq.com/reach-out for personalised on-boarding for your business.
Our team will work with you to help gather data from disparate data sources, use our platform to clean and prepare it for predictions.
Looking forward to hearing from you :)
Voyants
