Today, we’re gonna build a model to predict loan defaults.

We’ll start by:

1. Reviewing the dataset
2. We’ll then build a Machine Learning model for predicting loan defaults.
3. And instantly see how we can make predictions and share them with others on the team.

So let’s get started.

Number 1: Reviewing the dataset

In this dataset, every single row is a customer that has taken a loan.

And every column tells us something about that customer.

Like Age, Income, whether they’re a home owner or not, etc.

At the end, we have a column here called Status.

This tells us if a customer in that row has repaid or defaulted on their loan.

This is a historical dataset.

And we now wanna use it to predict if a new customer is likely to default.

Number 2: Building the machine learning model

To build a model, we start by uploading this CSV.. We can upload spreadsheets and connect to apps & services like Salesforce and Dropbox and even databases like MySQL and BigQuery.

Once uploaded, all you have to do is select your prediction column.  In this case, it’s the Status column. From here, Obviously AI will automatically build a machine learning model to make predictions, in less than 1 minute.

Number 3: Make predictions and share with your team

Once your model is built, it’s ready to be used right away. No deploying, no maintenance. You can see it’s accuracy and performance details here. 

Seems like we’re good to go!

We start by heading to Personas to make a first prediction Let’s say we have a new customer. They’re 32 years old, have an income of 85,000 dollars, are looking for a loan of 10,000 dollars and have a loan percent income of 0.5.

As I change these details, I can see the prediction for this new customer come up right here. Probability to repay is 75%!

Now, let’s head to export predictions. We can upload a list of new customers and get a prediction on each one. An API can also be used to automate these predictions and embed them in your own app or website.

You will also have the option to get a shareable link to send to anyone on your team. It will enable them to use the model you built to start making their own predictions.

So, what would you like to predict today?