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Decision trees are a fundamental machine learning technique that every data scientist should know. Luckily, the construction and implementation of decision trees in SAS is straightforward and easy to produce. The data that we will use for this example is found in the fantastic UCI Machine Learning Repository. The data set is titled "Bank Marketing Dataset," and it can be found at: http://archive.ics.uci.edu/ml/datasets/Bank This data set represents a direct marketing campaign (phone calls) conducted by a Portuguese banking institution.

Here is a link to the podcast. Vicki: Thank you so much for having me. Hugo: It's an absolute pleasure to have you on the show. I'm really excited to talk about your work in Python education, full stack data science, end-to-end data science, what these things actually mean, and your work in consulting. Before we get into all of that, I'd love to know a bit about you. I'm wondering what you're known for in the data community. Vicki: Probably first and foremost, terrible puns and memes about all sorts of data and programming related things. My strategy is a little bit like BuzzFeed, right? Hit them with the memes and then sneak in serious content in between. Vicki: I've written a lot of blog posts about how to do specific things in Python, how to do specific things in data, and then just talking about like where we are in the data community in general, so very high level articles, and talking about things that break down complicated concepts into easy to understand analogies. I love that secondary is the content and that primary are terrible puns and memes. I don't mean to put you on the spot, but what's one of the worst puns you've said or come up with or heard? I have this series of puns where it's basically me pretending to talk to a TV producer to pitch them on possible shows or movies, and so that series is s pretty terrible series of tweets. I thought I would just mention that you're also, in terms of content, in the process of creating a DataCamp course.

We'll find weather data, explore it and get it in order. We'll use the modeling tools of deseasonalization and linear regression to predict temperatures at the beach. We'll use the statistical tools of autoregression and confidence intervals to guide our feature selection and apply our results. And we'll code the whole thing up from scratch in python and organize it to be easy to read and easy to extend. When you're done, you'll have a standalone weather predictor that can estimate high temperatures three days from now.