Alexander Pope is famously quoted as saying: A little learning is a dangerous thing; drink deep, or taste not the Pierian spring: there shallow draughts intoxicate the brain, and drinking largely sobers us again. I've been thinking about these words the past few days as I worked on my latest challenge: a text classifier using... Continue Reading →

It's all very well downloading complex datasets from Kaggle and similar sources to play with - they're amazing for learners because the data is always less clean than you would have hoped, more complex than you anticipated, and every bit as interesting as promised. BUT if you're learning a new concept it's easier to have... Continue Reading →

Once you have your districts drawn up nicely, using the polygons from your shapefile, it would be useful to be able to label them - but of course you need to be able to tell GeoPandas where to place these labels via co-ordinates or points - and in your shapefile you only have polygons which... Continue Reading →

Dear World, Please send me more geographical data to plot so I can keep on using GeoPandas... Love from Sho't Left I can't believe how much fun this library is! So my goal was to find a way to map assessment ratings by region, showing the overall result for the region, as well as the... Continue Reading →

GeoPandas gets more and more exciting as the day wears on :). I've just discovered how ridiculously easy it is to take a set of cities and relate them to their corresponding districts, states or provinces and then plot the outcomes. Fanfare for... the marvellous GeoPandas spatial join! Here's a quick how-to guide to setting... Continue Reading →

So: with linear regression (aka simple linear regression) we have one feature which we are using to predict a dependent value (for example number of rooms as a predictor of house price). With multivariate regression (aka multiple linear regression) we simply have multiple features which could be used to predict that dependent value (for example... Continue Reading →

Polynomial regression is a considered a special case of linear regression where higher order powers (x2, x3, etc.) of an independent variable are included. It's appropriate where your data may best be fitted to some sort of curve rather than a simple straight line. The polynomial module of numpy is easily used to explore fitting the best... Continue Reading →

Typically we have 2 sets of values and we want to find out if these 2 sets of values are related, and if so how, and by how much? Could height be indicative of weight? Could hours of practice be related to how many errors are made in a mathematical test paper? Co-variance is a... Continue Reading →

I have a "real" assignment (for work as opposed to study) to do some data visualizations using maps. It's been a journey of over a week to get to the place where I'm ready to start, and the journey has had some educational detours along the way that I thought I'd share. Naturally, because I'm... Continue Reading →