Simple machine learning in R - Decision Trees

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

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.

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Pivot Chart Tracker

Sales mix by sale type
One of the dashboard charts

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.
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