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.
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
Following on from the previous Land Registry posts I've had a go at using the GoogleVis package to plot the data. So far my favourite method is the motion chart below. Although I need to make more equal sales areas as London ruins the x-axis a bit. The best view is the histogram view. Read More
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
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.
I was setting up some trackers at work the other day using some OLAP cubes in Excel across a number of different variables (about 20) to track monthly sales which I could refresh each month. Once I’d set the sales tracker up I realised that I wanted to look at average price across the same variables so I made a copy of the spreadsheet and went through each of the pivot tables changing them to track average price. When I then wanted to look at sales mix (%sales that month) I thought there must be a better way so decided to write some VBA to do all of this for me.