Over a million developers have joined DZone.

Watson Machine Learning Sample for Developers

DZone's Guide to

Watson Machine Learning Sample for Developers

You don't have to be a data scientist to use Watson Machine Learning! Get a dev-friendly walkthrough with an example that uses the Titanic dataset.

· AI Zone ·
Free Resource

Did you know that 50- 80% of your enterprise business processes can be automated with AssistEdge?  Identify processes, deploy bots and scale effortlessly with AssistEdge.

Last month, IBM announced the general availability of Watson Machine Learning, which can be used by data scientists to create models and used by developers to run predictions from their applications. Below is a simple sample walkthrough.

As a sample scenario, I've chosen the Titanic dataset to predict whether people would have survived based on their age, ticket class, sex, and the number of siblings and spouses aboard the Titanic. I picked this dataset because it seems to be used a lot in tutorials and demos how to do machine learning.

There are different ways for data scientists to create models with Watson Machine Learning. I've used the simplest approach. With the Model Builder, you can create models with a graphical interface without having to write code or understand machine learning.

First, I created a new model.

Next, I uploaded the Titanic dataset (.csv file).

At the top of the next page, the label (column to predict) is defined — in this case, "survived." Below the label, the list of features like age and ticket class is specified. Since in this sample I want to predict "survived" or "not survived," I choose binary classification. Since I'm not a data scientist, I didn't know which estimators to use and selected all of them.

After the training has been completed, you can see the results of the different estimators, select the best one, and save the model.

In order to run predictions from applications, the model needs to be deployed (bottom of the screenshot).

Once deployed, an endpoint is provided to invoke POST requests with input features which returns the prediction. Check out the API Explorer for details.

To learn more about Watson Machine Learning, open IBM Data Science Experience and give it a try.

Consuming AI in byte sized applications is the best way to transform digitally. #BuiltOnAI, EdgeVerve’s business application, provides you with everything you need to plug & play AI into your enterprise.  Learn more.

ai ,watson ,machine learning ,data science ,tutorial ,predictive analytics

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}