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 a couple of graphs later my suspicion was confirmed: If Alice Springs and Adelaide had a baby and it was exactly like Adelaide in every way it would be a great place.
This post takes some of 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 is. The truth is, the NT is mostly absent from national conversation that occurs in nationwide newspapers, telecasts, and the like. Why is that?
In this notebook, I’m going to look at the basics of cleaning data with Python. I will be using a dataset of people involved in the Enron scandal. I first saw this dataset in the Intro to Machine Learning class at Udacity.
Pathlib is a built-in Python library that is similar to os.path but contains a lot more. This post walks through some of the basics with pathlib.