I was watching a later series of QI recently and couldn’t help but notice that Alan Davies was winning quite a few episodes. That prompted me to ask the question: is Alan Davies getting better at QI?
I’ve often read the advice that side projects should be solving problems or answering questions that you yourself are interested in. To that end, I’ve always wanted to know how well the Hungarian national team have done against various countries worldwide and to explore this question, I scraped the matches played by the Hungarian national team and made an interactive world map.
Having an accurate machine learning model may be enough in itself, but in some cases the only way to turn it into a business decision is if you can understand why it’s getting the results it’s getting. In this short tutorial I want to show a quick way to visualise a trained decision tree in Python.
In this post I look at a practical example of k-means clustering in action, namely to draw puppies. I also touch on a couple of more general points to consider when using clustering.
As part of my commitment to occasionally talk about “programming for data scientists”, I want to share ideas that will facilitate this to help data scientists focus on important stuff. In this post I want to share some thoughts on how to make your Jupyter notebooks easier to “productionise”.
My first attempt to bridge the gap between the two disciplines of programming and data science, by talking about programming concepts useful for data scientists, and vice versa. Today: duck typing.
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