Machine Learning -Part IV – Create a Machine Learning Model using Azure Machine Learning Studio

Sathish Nadarajan
Solution Architect
February 5, 2018
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In the earlier articles, we saw the introduction about the machine learning and various key concepts, terminologies of Machine learning. In this article, let us see, how to create a Demo Model on Azure Machine Learning Studio.

Before start creating the Demo, let us see What is Azure Machine Learning Studio is.

Azure Machine Learning Studio

· It is a Visual Editor for Composing, Testing, Refining and deploying Machine Learning models

· ML Studio Includes

Ø Hundreds of Modules

Ø Common Algorithms for Classification, Regression and more

· Supports numerous input formats

· The custom algorithms can be built any few languages like R and Python.

ML Studio simplifies machine learning by providing a drag-and-drop model in which you build workflow. With ML Studio and the rich of assortment of modules it offers for modeling workflow, you can often build sophisticated models without writing a single line of code. However, it allows you to insert R and Python code anywhere in the workflow, providing infinite flexibility in what you can model.

With this introduction, let us create a Demo. As usual, let us go by Step by Step procedure

1. Login to the Azure ML Studio Portal –


2. Go to the Experiments Tab on the left quick launch and we could see the existing experiments.

3. Let us click on the New Button on the Bottom ribbon.

4. There are many prebuild templates are available. Let us create with a blank experiment.


5. Click on the Blank Experiment. We can see the blank Canvas as below.


6. Now, before proceeding, let us know, what we are going to do.

a. I am going to feed the model with the historical data of various cars.

b. Cleansing of the data to make it more meaningful

c. Split the data – 75% for the training and 25% for evaluation

d. Train the 75% of the splitted data with Linear Regression Algorithm

e. Evaluate the model

f. Deploy the model as web service.

g. Predict the Price of the Car.

7. With this, let us drag and drop the “Automobile price data” from the left hand side as show in the below image.



8. Search for Clean in the search experiment items and select the “Clean Missing Data”


9. Connect the Raw data to the Clean Data module. A simple mouse click and drag the link will do the below.


10. Now, Search for Select Columns in DataSet and drag and drop the item.


11. Connect the output of the cleaned data set to the Select Columns Item.


12. Now, select the “Select Columns in Dataset” and on the property pane, click launch column selector.


13. On the Popup, select the columns which we are going to consider for this model.


14. The final selected columns are as shown below.


15. Now, let us run the model to see the output as of now.

16. Click the Run button at the bottom Ribbon.

17. After the Run, we can visualize the dataset.


18. The Cleaned dataset will be as below.


We can export this dataset as well.

19. Add the “Split Data” function to the model and give the Fraction of rows as .75 on the right pane. i.e., 75% of the data should be splited to one leg and the rest 25 % on the other leg.


20. Then drag and drop the “Train Model”



21. On the Train Model Property Pane, select the Price column, because that is the value which we are going to predict.


22. Add the Score Model and Evaluate Model.


23. With this, we are done with creating the Model. Let us run the Model by clicking the Run on the Bottom.

24. The total time taken for training with the given data is less than a minute. This time will vary based on the volume of the dataset given as input.


In the upcoming article, let us see how to convert this as a web service and consume it from our Application.

Happy Coding,

Sathish Nadarajan.

Author Info

Sathish Nadarajan
Solution Architect
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Sathish is a Microsoft MVP for SharePoint (Office Servers and Services) having 13+ years of experience in Microsoft Technologies. He holds a Masters Degree in Computer Aided Design and Business more

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