201412 Predictive Analytics Foundation course extract

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Predictive Analytics: Extracts from Red Olive foundational course

For more details or to speak about a tailored course for your organisation please contact:

Jefferson Lynch: [email protected] +44 1256 831100

December 2014

Analytics and Data Management 1

mailto:[email protected]



Contents

What makes a great analysis?

Measuring relationships between variables

Profiling

What is data mining?

The data mining process

Data mining techniques

Discussion – next steps for data mining

Back-up slides

Introduction to descriptive statistics

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Some examples


Christmas 2011

Queen’s Diamond Jubilee

Road works ban starts

(1st July 2012)

London 2012 Olympic and Paralympic

Games

Winter-time road works /

end FY

Monitoring Trends Traffic Disruption in London

Information from Transport for London

Oracle Day presentation, 6 Nov 2012 Copyright © 2014 Red Olive Ltd, All Rights Reserved. 4

Geographical Mash-ups

5

Visualising:

Connections between businesses in East London

Based on:

Streams of Twitter data, tracking relationships, mentions

and retweeets

Source: http://www.techcitymap.com/index.html#/

Copyright © 2014 Red Olive Ltd, All Rights Reserved.

http://www.techcitymap.com/index.html

Census Analysis


Original source: ONS data visualisation centre

http://www.ons.gov.uk/ons/interactive/index.html

Census 2011: Explore population changes in your area

Source: The Telegraph online Interactive tool for looking comparing areas on their 2001 and 2011 demographic profiles

http://www.telegraph.co.uk/ear

th/greenpolitics/population/9403239/Census-2011-Explore-the-population-changes-in-your-area.html

http://www.ons.gov.uk/ons/interactive/index.html

http://www.telegraph.co.uk/earth/greenpolitics/population/9403239/Census-2011-Explore-the-population-changes-in-your-area.html



















Measuring relationships between variables • In order to start making ‘connections’ we need to investigate

relationships between variables

• Start point - relationships between two variables at time

• Multivariate techniques allow us to investigate relationships between many variables

• The appropriate measure of relationship depends on the type of data that you’re analysing – primarily whether scale (numeric) or nominal (categorical)


Measures of relationship Scale (numeric) data

Correlation quantifies the linear relationship between variables in scatter plots

+1 = exact positive relationship e.g. e.g.

0 = no relationship e.g.

x x x

x x

x

x x x

-1 = exact negative relationship e.g.


Correlation coefficient takes values between -1 and +1

The correlation will rarely be exactly 1 or -1 This would suggest that the variables were exactly dependent on each other

Likewise the correlation is rarely exactly 0 Because a slight relationship can occur by chance

Correlation measures the extent of a linear relationship, so needs to be handled with care

Four sets of data with

the same correlation of 0.816

For Correlation:

Excel function CORREL


http://en.wikipedia.org/wiki/File:Anscombe.svg

What is data mining?


Two main types of data mining model

Type 1: Models driven by a Target Variable e.g. Which site visitors are likely to subscribe? - Implies building a Predictive Model - ‘Directed’ Data Mining Techniques

Type 2: Models with no Target Variable e.g. How does the subscriber base segment? - Implies a Descriptive Model - ‘Undirected’ Data Mining Techniques


Gains Chart – based on representative evaluation sample

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Cu

mu

lati

ve %

oo

f re

sp

oo

nd

en

ts

Cumulative % of base

Gains Chart Churn Model

prediction

random

optimal


Data mining techniques and where they can be applied


Techniques to be discussed

Predictive Forecasting

Decision trees Regression models

Descriptive

Factor analysis

Cluster analysis Affinity analysis


Techniques on individual-level data Data mining methods


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Target Variable: Good/Bad Credit Rating

Best predictor: Income Level

2nd best predictor: Number of credit cards

End nodes: No further splits

Example Decision Tree

Final predictor: Age

Highly significant

Copyright © 2014 Red Olive Ltd, All

Rights Reserved. 16

The affinity tile map

Strengths of affinities are displayed using a ‘hot-cold’ colour palette By clicking on a tile, details of the pair of products and their affinity are revealed

Source: Teradata Copyright © 2014 Red Olive Ltd, All Rights Reserved. 18

201412 Predictive Analytics Foundation course extract

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