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.
Successfully complete this lab by achieving the following learning objectives:
- 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.
- 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.
- 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.
- 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.
- 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.