Machine learning models must be trained with existing data. In this case, you'll use a dataset of historical bicycle rental details to train a model that predicts the number of bicycle rentals that should be expected on a given day, based on seasonal and meteorological features.

Create a dataset

In Azure Machine Learning, data for model training and other operations is usually encapsulated in an object called a dataset .

1. View the comma-separated data at https://aka.ms/bike-rentals in your web browser. Then save this as a local file named daily-bike-share.csv (it doesn't matter where you save it).

2. In Azure Machine Learning studio , view the Datasets page. Datasets represent specific data files or tables that you plan to work with in Azure ML.

3. Create a new dataset from local files, using the following settings:

Basic Info :

• Name : bike-rentals

• Dataset type : Tabular

• Description : Bicycle rental data

Datastore and file selection :

• Select or create a datastore : Currently selected datastore

• Select files for your dataset : Browse to the daily-bike-share.csv file you downloaded.

• Upload path : Leave the default selection

• Skip data validation : Not selected

Settings and preview :

• File format : Delimited

• Delimiter : Comma

• Encoding : UTF-8

• Column headers : Only first file has headers

• Skip rows : None

Schema :

• Include all columns other than Path

• Review the automatically detected types

Confirm details :

• Do not profile the dataset after creation

4. After the dataset has been created, open it and view the Explore page to see a sample of the data. This data contains historical features and labels for bike rentals.

Citation : This data is derived from Capital Bikeshare and is used in accordance with the published data license agreement .