1. Predicting Churn with No-Code Machine Learning

Today, we’re gonna build a model to predict customer churn. 

We’ll start by:

  1. Reviewing the dataset
  2. We’ll then build a Machine Learning model for predicting churn
  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 individual customer.

And every column tells us something about that customer.

Like Gender, Dependents, Senior Citizen etc.

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

This tells us if a customer in that row has churned, Yes or No.

This is a historical dataset.

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

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 Churn 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.

She is a female, is a senior citizen, uses a fiber optic connection and pays about 50 bucks a month, via a credit card.

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

Probability to churn is 69%!

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.We can now head to export predictions, where we can upload a list of new customers and get a prediction on each one of them. We can also use an API to automate these predictions and embed them in your own app or website.

And most importantly, you get a shareable link to a web app! This means people that don’t even know what Obviously AI is, can now use the model you built to start making their own predictions.

So, what would you like to predict today?



Complete and Continue