6. Predicting Employee Attrition with No Code Machine Learning

Today, we’re gonna build a model to predict employee attrition.

We’ll start by:

  1. Reviewing the dataset
  2. We’ll then build a Machine Learning model for predicting employee attrition.
  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 an employee at a company.

And every column tells us something about that employee.

Like Age, education, years at company, etc. 

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

This tells us if an employee in that row is currently with the company. Yes or No

This is a historical dataset.

And we now wanna use it to predict if a current employee is likely to churn or not.

Number 2: Building the machine learning model

To build a model, we start by uploading this CSV. 

We can upload spreadsheets,

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 Attrition 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 current employee.

They’re 32 years old, are in a Research & Development department, have a technical degree, they were employee number 12 at the company and are paid $800 a day. 

As I change these details, I can see the prediction for this employee come up right here.

Probability of them quitting their job is 86%.

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?

Complete and Continue