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.
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.
In my first land registry post I imported a month’s worth of land registry data, named the rows and had a go at using the ggplot2 package to produce a number of nice looking charts. This time I want to progress a little further. My aims are, using the same dataset to:
- Look at the distribution of prices
- Look at the prices by different factors
- Initially just using factors in the land registry data