UCI Bike Rental.pdf - Overview In this lab you will train...

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Overview In this lab, you will train and evaluate a regression model. Regression is a supervised machine learning technique in which a set of data with known labels is used to train and test a model. Regression predicts a real numeric value for the label. In this lab you will use the data set provided to predict the demand in a bicycle rental system . The steps in this process include: Explore the data set. Create a linear regression model. Evaluate the model using both computed metrics and visualization. What You’ll Need To complete this lab, you will need the following: An Azure ML account A web browser and Internet connection The lab files for this lab Note: To set up the required environment for the lab, follow the instructions in the Setup Guide for this course. Exploring the Data In this lab you will work with a dataset that contains records of bike rentals. Visualize the Source Data The bike rentals data set is provided as a sample data set in Azure Machine Learning. 1. If you have not already done so, open a browser and browse to . Then sign in using the Microsoft account associated with your Azure ML account. 2. Create a new blank experiment, and give it the title Bike Regression. 3. In the list of experiment items, expand Saved Datasets and Samples, and then drag Bike Rental UCI dataset to the experiment canvas. 4. Visualize the output of the dataset and review the columns it contains. Note that these include various columns containing temporal and meteorological information for each hour, starting at 00:00 on January 1st 2011. Additionally, the dataset includes the hourly count of bike rentals by casual and registered customers, with the total hourly rentals recorded in the cnt column. Your
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