This post demonstrates some basic techniques for the Python library pandas.
The story of Schrödinger’s cat, a cat that, through quantum physics, is simultaneously alive and dead, has become ingrained in popular culture and many popular science articles. But as the physics behind it has become popularized, misconceptions have been introduced into the story. When you Google “Schrodinger’s cat” the following definition appears: “a cat imagined as being enclosed in a box with a radioactive source and a poison that will be released when the source (unpredictably) emits radiation, the cat being considered (according to quantum mechanics) to be simultaneously both dead and alive until the box is opened and the cat observed.” The notion that a cat can be both alive and dead at the same time is counterintuitive and, most importantly, completely false. I propose a thought experiment that demonstrates that the popular conception of a half-living cat is impossible.
A short walk around the Adelaide CBD was enough to start me wondering if Adelaide’s climate wasn’t better than that of Alice Springs. It seemed that no matter the time of year, the weather in Adelaide was much nicer than Alice. I grabbed some climate data from Wikipedia, and after a couple of graphs, my suspicion was confirmed: If Alice Springs and Adelaide had a baby that was exactly like Adelaide in every way, it would be a great place.
This post takes some of the algorithms that we saw in the previous post and shows how they perform on the gains charts. Gains charts, which are also called lift charts, are a good way to see how much lift an algorithm has over guessing.
This notebook takes over from part I, where we explored the iris dataset. This time, we’ll give a visual tour of some of the primary machine learning algorithms used in supervised learning, along with a high-level explanation of the algorithms.
In this notebook, we’ll demonstrate some data exploration techniques using the famous iris dataset. In the second notebook, we’ll use this data set to visualize a bunch of machine learning algorithms.
How long do you have to read or watch Australian news before there’s any mention of the Northern Territory? Or someone living here, from here, or even anyone who’s ever been here? It shouldn’t take that long, but I assure you, it does. The truth is, the NT is mostly absent from national conversation that occurs in nationwide newspapers, telecasts, and the like. Why is that?
