Using Azure Machine Learning to Predict Who Will Survive the Titanic


One of my “Introduction to Azure Machine Learning” talks demonstrates how to use Azure Machine Learning to make predictions.  The example I use is predicting whether a passenger on the Titanic will survive, given information like their age, gender, class of ticket, ticket fare, etc.  (You can download the Titanic dataset from Kaggle.)  But these same principles can be used to predict if someone will make a purchase online or whether a patient will be readmitted to a hospital in the next 30 days.

In Part 1, I demonstrate how to upload a dataset into Azure Machine Learning Studio, explore the data and decide how to modify it, and use data cleaning modules to implement these changes. 


Then, in Part 2, I train a model with a machine learning algorithm, deploy our model, and call our published model to get results.

Comments (7)

  1. This is by far the most interesting article I read today. Amazing.

  2. Jaroslav Karlik says:

    I really enjoyed watching this demo. Big thanks for introducing the Kaggle to me as well as I disnt know about it and now and browsing it every day

  3. jennmar says:

    Thanks!  I'm glad it was useful.  

  4. Khaled Hikmat says:

    Bravo! Very good and helpful presentation.

  5. Khaled Hikmat says:

    Hi Jennifer, how do you handle changes to the input data? Say for example, my data needs to be refreshed weekly or monthly. Thanks.

    1. jennmar says:

      Great question. You can programmatically update your model with fresh data: http://azure.microsoft.com/en-us/documentation/articles/machine-learning-retrain-models-programmatically/ (I published an article on getting started with AzureML at http://aka.ms/HackML that might also be useful.)

      1. Khaled Hikmat says:

        Thank you very much.

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