During this demo-rich session you will learn how to use Azure Machine Learning Studio and Python to build a quality predictive model, while competing in a live machine learning competition, hosted by Kaggle - the Titanic competition.
You will find out how to analyse and model your data in order to maximize its predictive power, how to build a predictive model, and how to evaluate its performance, all in a browser-based, visual environment that’s both powerful and simple.
Long title, I know 🤫. It used to be shorter, as some earlier versions of this talk were called ‘Predicting Survivability on the Titanic’, but this time I wanted to experiment a bit and make it real easy for the audience to decide whether or not this would be interesting for them. And so they did.
You see, they wanted to learn more about machine learning. And, the way I see it, the two tools I talked about - Azure Machine Learning Studio and Kaggle Competitions - can help you get started with ML, while also making it fun to do so.
So we proceeded with actually competing live in the Titanic: Machine Learning from Disaster starter competition, downloading the passengers dataset, training a very simple (and overly optimistic) model, a model which crashed and burned when pit against the other participants in the competition 🤭, learning from our mistakes and gradually fixing the issues with the dataset, creating new features, improving the model, and in the end achieving a top 20% score (which, I know, could have been better, but hey, we only had 1 hour to achieve all of this 😉).
Apart from achieving this, I must say I absolutely loved interacting with the audience, answering their questions and discussing the various approaches of parsing data, doing feature engineering, picking the right algorithm, and evaluating a model. It was awesome, and I’m very grateful for that 😁.