I thought I was done with this but I’m not. Time to have more of a play in R with Land registry data and the National Statistics Postcode Lookup (NSPL). You never know maps and networks might also be involved.
I’ve posted before on this and I clearly didn’t know what I was doing. I still don’t really know what I’m doing but I now have some pretty pictures and that’s all anybody really wants. In this post I’m going to import a postcode shapefile from the OS, plot the postcodes in R, find the neighbours of each postcode and convert the data into a network graph. Github repository here.
During the world cup I ran a competition to see who on the team was the best at predicting the results of world cup. Unlike everyone else in my company my competition used R Shiny as an interface and was hosted on an Amazon virtual machine.
In the last two posts I created some simple decision trees and tested their accuracy. Now it’s time to try some other models. As before I’m going to continue predicting the variable FiveHundredPlus with a limited set of factors to keep the processing pressures down. Once I’m a bit more confident I’ll move to the larger dataset and a more powerful machine. I’m going to use the package caret and recreate this post from Analytics Vidhya.
Full code saved on my github page here.
A quick follow up on the last post. I forgot to write about plotting ROC curves in R based on the different models. In the last post I created 5 progressively more complicated decision trees which didn’t really add any benefit when looking at the accuracy of the model. But accuracy is just one metric, what do the ROC curves look like and what are the areas under the curves? Read More
Now that the land registry data has been imported and had some initial exploratory work done to it lets have a go at making a price prediction model. I’ll use a small subset of the data and initially only try to predict whether or not the house is worth more or less than £500k, rather than the more complicated process of predicting the price. The code used in this post is largely based upon the DataCamp course “Introduction to Machine Learning”. Code for this project is on my GitHub page here. This post focuses on decision trees using the package rpart. Read More
My computer has been struggling with some of the code I’ve been trying to run, it is pretty old and doesn’t have enough memory for large datasets in R. So rather than buy a better laptop I’ve set up an Amazon Web Service account and using this guide set up a computer so I don’t have to use mine. I’m only using the free one for now but if I want to have a go at processing something larger this will allow me to pay a small fee to use a more powerful machine for a short period of time.
After my last post on the ONS data structure this post is the first of a few on using that structure and some other public data, mostly UK government data, and mapping it using R. This first post is about getting shapefiles from various locations, loading them into R and plotting them.
I have been looking through the ONS geographic data on their Geo Portal and there are acronyms and variables everywhere so I thought it best to understand what they all mean. Whenever I refer to the output areas and super output areas I’m referring to the ones as at the 2011 census in England and Wales.