Suppose we ran an A/B test with two different versions of a web page, \(a\) and \(b\), for which we count the number of visitors and whether they convert or not. We can summarize this in a contingency table showing the frequency distribution of the events: ... Continue reading.

I was reading an article about visualization techniques using multidimensional scaling (MDS), the correspondence analysis in particular. The example used R, but as usual, I want to find ways to do it with Python, so here goes. The correspondence analysis is useful when you have a two-way contingency table for which relative values of ratio-scaled data are of interest. ... Continue reading.

There are several ways to run principal component analysis (PCA) using various packages (scikit-learn, statsmodels, etc.) or even just rolling out your own through singular-value decomposition and such. Visualizing the PCA result can be done through a biplot. I was looking at an example of using prcomp and biplot in R, but it does not seem like there is a comparable plug-and-play way of generating a biplot with Python. ... Continue reading.