Applied Machine Learning Lab 2 – Working with Spatial Data
Overview In this lab, you will use R to work with spatial data. Specifically, you will use Kriging to interpolate density values in a spatial data frame.
What You’ll Need To complete this lab, you will need the following: An Azure ML account The 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 Spatial Data In this exercise, you will explore the Meuse sample dataset, which contains data about heavy metal concentrations in the river Meuse in Belgium.
Upload a Jupyter Notebook 1.
Browse to https://studio.azureml.net and sign in using the Microsoft account associated with your free Azure ML account. 2. If the Welcome page is displayed, close it by clicking the OK icon (which looks like a checkmark). Then, if the New page (containing a collection of Microsoft samples) is displayed, close it by clicking the Close icon (which looks like an X). 3. In Azure ML Studio, click NEW; and in the NEW dialog box, in the NOTEBOOK tab, click Upload. Then in the Upload a new notebook dialog box, browse to select the Kriging.ipynb file from the folder where you extracted the lab files on your local computer. Enter the following details, and then click the icon. Enter a name for the new notebook: Kriging Select a language for the new notebook: R 4. Wait for the upload of the notebook to complete, then click OK on the status bar at the bottom of the Azure ML Studio page.
Explore Spatial Data 1. In Azure ML Studio, on the Notebooks tab, open the Kriging notebook you uploaded in the previous procedure.
2. Follow the instructions in the notebook to work with the spatial data. 3. When you have completed all of the coding tasks in the notebook, save your changes and then close and halt the notebook.
Summary In this lab, you have used R in a Jupyter notebook to work with spatial data.
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Developing SQL Databases. Lab 4 â Creating Indexes. Overview. A table named Opportunity has recently been added to the DirectMarketing schema within the database, but it has no constraints in place. In this lab, you will implement the required cons
create a new folder named iislogs in the root of your Azure Data Lake store. 4. Open the newly created iislogs folder. Then click Upload, and upload the 2008-01.txt file you viewed previously. Create a Job. Now that you have uploaded the source data
will create. The Azure ML Web service you will create is based on a dataset that you will import into. Azure ML Studio and is designed to perform an energy efficiency regression experiment. What You'll Need. To complete this lab, you will need the fo
Lab 2 â Using a U-SQL Catalog. Overview. In this lab, you will create an Azure Data Lake database that contains some tables and views for ongoing big data processing and reporting. What You'll Need. To complete the labs, you will need the following
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In this lab, you will explore and visualize the data Rosie recorded. ... you will use the Data Analysis Pack in Excel to apply some statistical functions to Rosie's.
