Lab
A Cloud Guru

Creating a Regression Model with Azure ML Designer

In this hands-on lab, you are working as a data scientist for Bike4Ever, a bike rental company. You’ve been asked to come up with a model that can be used to predict bike rentals based on factors such as weather, temperature, day of the week, and more. You’ll use Azure Machine Learning studio and Azure Machine Learning designer to create a regression model that will predict bike rentals.

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Path Info

Level

Beginner

Duration

1h 0m

Published

Dec 16, 2021

Challenge

Open the Azure ML Studio and Create a New Dataset

In the Azure ML studio, create a new dataset using the bike rental data file from Microsoft.
Challenge

Review the Dataset

Take a look at the data you’re working with and see if there are any missing values or values that will cause issues.
Challenge

Design a Pipeline to Predict Bike Rentals

Using the Azure Machine Learning designer, drag and drop modules to create a pipeline that will train a regression model to predict bike rentals. When setting the compute type, set it to Compute cluster.
Challenge

Review the Results

Take a look at some of the metrics for the run. For example, the R2 score or the root mean squared error.
Challenge

Bonus: Optimization of the Model

As a fun bonus objective, feel free to tweak some settings in your pipeline to see if you can get better scores for your model.

Author

A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

What's a lab?

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.