coppelia machine learning and analytics coppeliaMLA

Return on investment is often measured as revenue divided by costs, sometimes expressed as a percentage. For example if a marketing campaign cost £10K but brought in £20K of additional revenue then the ROI is 200%. Now if you are just given the ROI you'll find you are missing any of idea of scale. The same ROI could be achieved with a revenue of £200 and with one of £200 million. So it would be nice to see cost, revenue and ROI visualised all in one go. There are a few ways to do this but after playing around I came up with the following representation which personally I like the best. It's a simple scatterplot of cost against revenue but since all points on straight lines radiating from the origin have the same ROI it's easy to overlay that information. If r is the ROI the the angle of the corresponding spoke is arctan(r).

Note you can drag about the labels. That's my preferred solution for messy scatterplot labelling.

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	<script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.2.1/d3.min.js"></script>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css"/>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
	<style> #mapid {
	height: 800px;
	} </style>
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	<body>

	#include <Servo.h>

	Servo middle, left, right, claw ; // creates 4 "servo objects"

	//positions 30, 45, 60, 75, 90, 105, 120, 135, 150

	void setup()
	{
	middle.attach(8);
	left.attach(10);

	<head>
	<script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css" />
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
	<style> #mapid {
	height: 800px;
	} </style>
	</head>
	<body>

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	<script src="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.js"></script>
	<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet/v0.7.7/leaflet.css"/>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.16/d3.min.js"></script>
	<style> #mapid {
	height: 800px;
	} </style>
	</head>
	<body>

	{
	"name": "root",
	"visits": "202K",
	"children": [
	{
	"name": "purpledog.com",
	"visits": "50K"
	},
	{
	"name": "squishedfish.co.uk",

	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>

	body {
	font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
	width: 960px;
	height: 500px;
	position: relative;
	}

	exp1<-rnorm(100000)
	exp2<-rnorm(100000)
	exp3<-rnorm(100000)
	noise<-rnorm(100000)
	lin.pred<-5exp1+2exp2+6*exp3+noise
	response.prob<-1/(1+exp(-lin.pred))
	response<-as.numeric(response.prob>0.5)
	toy.data<-data.frame(response, exp1, exp2, exp3)
	write.csv(toy.data, 'ToyData.csv', row.names = FALSE)

	#Another simple test

	x.1<-rnorm(50, 10, 3)
	y.1<-rnorm(50, 10, 3)
	x.2<-rnorm(50, 20, 3)
	y.2<-rnorm(50, 10, 3)
	x.3<-rnorm(50, 13, 3)
	y.3<-rnorm(50, 20, 3)

	test.data<-data.frame(group=rep(1:3, each=50), x=c(x.1, x.2, x.3), y=c(y.1, y.2, y.3))

	#Testing on the iris data set
	iris.dist<-dist(iris[,1:4])
	bhc<-buster(iris.dist, n=250, k=3, size=0.66, method='ward', pct.exc=0.1)
	plot(bhc)

	#We see the unstable observations in pink.

	cluster<-bhc$obs.eval$cluster[order(bhc$obs.eval$obs.ind)]
	plot(iris[,1:4], col=6-cluster, pch = rep(15:17, each=50))