Predictive Analytics (PA) in Practice€¦ · Predictive Analytics (PA). • In particular, to: –...
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Predictive Analytics (PA) in Practice Steven Finlay 13 th September 2016
Transcript of Predictive Analytics (PA) in Practice€¦ · Predictive Analytics (PA). • In particular, to: –...
Predictive Analytics (PA) in Practice Steven Finlay 13th September 2016
Presenter
Presentation Notes
Hi My name is Steven Finlay. I’m head of Analytics at a company called HML. You have probably never heard of HML – but it’s Europes largest mortgage outsourcing provider, based in Skipton, Yorkshire. HML is part of the Computershare Group, which employs over 15,000 people at several sites around the world. We specialize in providing the infrastructure to support residential mortgage portfolios from origination through to debt recovery. We currently manage more than £35 billion assets for our customers. Prior to this role I worked for a number of organizations including UK Government and Experian. For the last 20 years or so I’ve spent most of my time building or designing decision making systems based around predictive analytics, but around 10-11 years ago I began to get interested in some of the ethical aspects what I was doing. Over time this has developed in to an approach to how I think about data and the how I advise others on the use that data, given its nature. A lot of what you are going to hear today is aligned some of the material in my latest book: Predictive analytics, Data Mining and Big data. Myths, misconceptions and Methods, Which will be published by Palgrave Macmillan in the next few weeks.
Objectives & Agenda
• Today I want to discuss some of the practical aspects of using Predictive Analytics (PA).
• In particular, to: – Provide a business perspective on how
Predictive Analytics is applied.
– Highlight some of the risk and issues that one may encounter when designing, building and implementing business solutions that incorporate predictive models, developed using predictive analytics.
1.Recap: What is predictive analytics?
2.Problem formulation (requirements planning)
3.Legal and reputational risks & issues that can arise when using predictive analytics.
4.Q & A
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Objectives Agenda topics
Appendix A. Recommended sources of further information about PA.
1. What is predictive analytics?
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Data set containing
dependent and independent
variables
Predictive Analytics
• Predictive analytics is the process of generating a predictive model via the analysis of a suitable data set.
• A predictive model is the result of the predictive analytics process. The model captures the relationships (correlations) that have been found between the dependent and independent variables.
Constant +598
Term of loan Number of children ≤ 12 months +51 0 0 13 – 17 months +28 1 – 2 +12 18 – 23 months +9 3+ 0 24 – 35 months 0 36 – 47 months -5 Occupation status 48 – 71 months -19 Full-time employed +7 72+ months -36 Part-time employed -22
Self employed -9 Accomodation status Homemaker -17
Owner +32 Student -47 Renting -17 Unemployed -84 Living at home 0 Retired +2
Time at current address Time in current employment < 1 year -68 < 1 year -59 1 – 2 years -29 1 – 2 years -23 3 - 5 years -11 3 – 4 years -14 6 – 10 years 0 5 – 7 years 0 11+ years +33 6 - 12 years 0 13 - 19 years +6
Gross annual income $ 20+ years +12 125,000+ +17 Not in employment 0 90,000 - 124,999 +11 50,000 - 89,999 0 Number of previous good paid loans 30,000 - 39,999 -26 0 -12 0 - 29,999 -49 1 0 2+ +17
Predictive Model
Definition of Predictive Analytics: “The application of quantitative techniques to predict future, or otherwise unknown behaviour, of individuals or other entities” (Finlay, 2014)
Presenter
Presentation Notes
The vast majority of applications relate to people. However the same techniques can be applied to other entities such as limited companies, football clubs etc. So, if we are talking about people, the independent data is typically this is a mixture of geodemographic data about people (age, gender, occupation, where they live) and transactional data – what they buy, where they go etc.
1. What is predictive analytics?
• The term “Predictive Analytics” (PA) became prominent in the mid-late 2000s. – PA can be considered a sub-set of Data Mining. To some extent, it can
be viewed as a rebranding of a range of Data Mining tools, applied to certain types of prediction/forecasting problem.
– Machine learning / AI use many of the same techniques • Albeit applied to a slightly different, but overlapping problem domain.
• Common techniques used in PA include: – Multivariate regression – Decision Trees / Classification and Regression Trees (CART) – (Artificial) Neural Networks (ANNs) – Support Vector Machines (SVMs) – Survival Analysis – Ensemble Methods
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Presenter
Presentation Notes
e.g. IBM’s Watson Voice recognition software / personal assistance such as Apple’s SIRI
1. What is predictive analytics? Common applications.
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Application1 Example / Description Type of prediction generated by the model
Response (choice) modelling
The probability that someone will respond to a marketing communication.
Classification
Date matching Producing lists of people who are likely to enjoy going on dates with each other.
Classification
Credit scoring The likelihood that an individual will repay a credit obligation (loan, credit card, mortgage etc.)
Classification
Tax evasion The probability that someone is deliberately paying too little tax.
Classification
Transaction fraud The probability of a credit card transaction being fraudulent.
Classification
Customer spend / profitability
The estimated amount that a customer will spend or the profit that a customer will generate.
Regression
Life expectancy The expected lifespan of an individual. Regression In residence The time when someone is expected to be in their home
(so best time to call/visit them). Regression
1. This is only a small number of examples of the application of PA. Eric Segal lists 127 different applications, and this is not an exhaustive list! (Segal, 2013)
2. Problem formulation (requirements planning)
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2. Problem formulation (requirements):
• Things usually start with a business problem.
• An organisation wants to do something new, or do something more efficiently. – PA is just one tool which may (or may
not) be useful for addressing that problem.
– A predictive model is often just one component of a wider decision making system. E.g. a credit card application processing system (diagram to the right).
– The model needs to fit within the requirements and constraints of that system.
– Insufficient understanding of the wider business requirement is one of the most common reasons why PA projects fail.
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Data management
unit
Scoring and strategy unit (Decision Engine)
Application processing system
Customer interface (customer contact centre/website/branch)
Applicant
Credit application Decision
Predictive model
Predictive modelling drives most credit Granting decisions (e.g. cards, loans)
External data sources e.g. credit reference agencies
Presenter
Presentation Notes
Important to also remember that the predictive model itself is only a tool. The model itself is not the end result. You have to do something with the model predictions. For example. We currently use expert underwriters to assess each and every credit application that we receive? Can we automate the decisions making process? So that it is quicker and cheaper? Example is where I once met a bunch of PhD computer scientists who had spent a huge amount of time devleoping a complex ANN model, only to then discover that the decision engine that their organisation used only catered for linear models and decision trees. Replacing the decisoin engine which cost in excess of £100K was not an option – so it was the model that had to be revisited.
2. Problem formulation (requirements)
• The need for good planning at the outset of a PA project is no different from other similarly complex projects in other domains. – You would never dream of asking a
programmer to just start coding and expect them to deliver useful operational software.
– You need an architect to design a building before letting the builders lose.
– Just handing over a load of data to a statistician/management scientist, and expecting them to deliver something useful is a very risky practice.
• In the remainder of this section we are going to look at some of the factors that one should consider before beginning any significant type of predictive analytics project.
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Key requirements to consider: • Definition of business and modelling Objectives. • Success criteria • Outliers and other model exclusions. • Dealing with missing data. • Forecast horizon. • Sampling / Sample size. • Choice of modelling approach. • Validation methodology. • Model usage (decision rules). • How is the model going to be implemented, and by whom? • Post implementation monitoring
Good practice is to write a “requirements Document” that contains all of these things, and to obtain sign-off from the key business stakeholders before proceeding further.
Presenter
Presentation Notes
Its worth spending time at this stage to understand and capture requirements Resist the temptation/pressure to “just get on with it” Given we only have an hour, I’m going to focus on the areas I’ve highlighted.
2. Problem formulation (requirements): Definition of business and modelling objectives
• Business objectives often: – Fuzzy / ill defined
– Qualitative / subjective
– Differs depending who you talk to
• Modelling objective (Dependent variable) – Simple
– Quantitative (a single number)
• Modelling objective (for classification) – Binary 0/1 value. E.g.
– Event = 1. Non-Event = 0
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Presenter
Presentation Notes
Predictive models predict simple, objective things. For classification models we are typical predicting binary “Yes/No” type outcomes
2. Problem formulation (requirements): Definition of business and modelling objectives
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• Imagine that we have been asked to build a model to identify tax evaders.
• Business objective:
– “We want an automated way of screening tax returns to identify cases were the return has been completed incorrectly, resulting in tax liability that is significantly lower than it should be.”
– “The cases identified will be passed to tax inspectors to investigate.”
– “We would therefore, like a model that predicts which tax returns are likely to generate significant additional tax yield when subject to detailed investigation.”
• To support the building of the model, the tax authority is willing to make all of its data available.
• However, it has initially provided details of a large number of historic tax investigations and their outcomes (if the tax return was correct or incorrect, and by how much).
• Its other databases are available on request.
2. Problem formulation (requirements): Definition of business and modelling objectives
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• Modelling Objective (dependent variable)
– A naïve model developer would now go away and build a model using the data provided, with the dependent variable defined as follows:
• Event (1) = Tax investigation resulting in £>0 additional tax liability being identified.
• Non-event (0) = Tax investigation resulting in £0 additional tax liability being identified.
• OK – what might be wrong with this formulation?
• Many things! For example:
– The business requirement referred to “significant” revenue. What does significant mean?
– The data provided only referred to the amount of the discrepancy identified. It did not report the actual amount of tax recovered.
– What about errors resulting in overpayment rather than underpayment?
Presenter
Presentation Notes
For this particular problem, the model developer was not naïve. Further discussion was undertaken to identify what significant meant (several thousand pounds) and also to bring in data about actual recovery amounts.
2. Problem formulation (requirements): Success criteria
• In the classroom, model performance is often measured soley (or mainly) in terms of predictive accuracy (discriminates well and is unbiased). – e.g. R-squared, GINI/Somer’s D/AUC, MAPE etc.
• These measures are important, but there are often other (business/qualitative) criteria, which must also be considered in practice. In particular: – How much money will I make / save?!
– Interpretability and common sense; i.e. can I understand how the model makes it predictions, and does the model structure conform to business expectation?
– Model complexity.
– Local performance in one region of the model’s prediction range (as opposed to global performance across the full range of predictions).
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Presenter
Presentation Notes
2. Problem formulation (requirements): Success criteria. Interpretability / common sense
• Do models always need to be interpretable?
– No, not always.
• However, business users and industry regulators are often reluctant to use models if they can’t understand how an individual prediction was arrived at.
• Common to represent linear models (e.g. those derived using linear or logistic regression) in the form of scorecards so to be easy for the layperson to understand.
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Constant +598
Term of loan Number of children ≤ 12 months +51 0 0 13 – 17 months +28 1 – 2 +12 18 – 23 months +9 3+ 0 24 – 35 months 0 36 – 47 months -5 Occupation status 48 – 71 months -19 Full-time employed +7 72+ months -36 Part-time employed -22
Self employed -9 Accomodation status Homemaker -17
Owner +32 Student -47 Renting -17 Unemployed -84 Living at home 0 Retired +2
Time at current address Time in current employment < 1 year -68 < 1 year -59 1 – 2 years -29 1 – 2 years -23 3 - 5 years -11 3 – 4 years -14 6 – 10 years 0 5 – 7 years 0 11+ years +33 6 - 12 years 0 13 - 19 years +6
Gross annual income $ 20+ years +12 125,000+ +17 Not in employment 0 90,000 - 124,999 +11 50,000 - 89,999 0 Number of previous good paid loans 30,000 - 39,999 -26 0 -12 0 - 29,999 -49 1 0 2+ +17
2. Problem formulation (requirements): Success criteria. Complexity
• A traditional response or credit scoring model, derived using stepwise regression techniques, may have less than 20 variables/parameters.
• Some types of predictive models have hundreds of thousands of parameters, based on complex ensemble methods. – These typically yield a 5-10% improvement in performance (GINI, R-
squared etc.) compared to a simpler, single models.
• The cost of implementation and maintenance of complex models can be a barrier to their use.
• Classic case is the Netflix prize (Amatriain X and Basilico J. 2012)
– Hailed as a great success, yielding a 10% improvement in discriminatory ability, yet was not deemed implementable.
• Also some evidence that the performance of complex models decays more rapidly than simple models (Hand 2006), requiring more frequent cycles of monitoring and redevelopment.
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2. Problem formulation (requirements): Success criteria. Local performance
• When we build models, measures of performance are typically based on global measures covering entire problem domain.
• However, its often within specific sub-sets of the problem domain where key decisions need to be made.
• An interesting case is where I was once asked to construct a model of voluntary repossession.
• The model predicted very well overall against industry standards. – GINI (AUC) 0.84
– 1,250 lift (model could identify one group of accounts which were 1,250 times more likely to voluntary repossess than the least likely).
• However, all we were interested in was the tail end of the distribution. The very worst 1-2% of cases, and the model was not able to provide a sufficient level of discrimination in this region.
• In the end the model was not used. 15
Presenter
Presentation Notes
In the mortgage market, a key driver of profitability is foreclosure (repossession of the mortgaged property). Mortgage providers use predictive analytics to estimate the likelihood of foreclosure. These predictions are then used to make decisions about how to manage customer relationships, such as whether or not to extend further borrowing (low probability of foreclosure combined with high equity) or when to initiate foreclose action, if an account is in arrears and it is unlikely that the customer will recover their ability to make repayments. These models are also very important for capital calculations that determine what assets a bank needs to hold in reserve to cover any unexpected loss events that might occur. For the majority of customers struggling to meet their mortgage repayments, the path to foreclosure is, sadly, pretty straightforward. They get behind with their repayments, various attempts are made to reschedule the debt, offer payment holidays and so forth to give the customer time to pay, but if the arrears continue to accrue then the mortgage provider will foreclose. However, a significant minority of foreclosures are voluntary. The customer hands back the keys to their property before any legal proceedings are undertaken. The customer has concluded that there is no realistic way for them to make their repayments. Therefore, they decide to end things as quickly as possible and make a fresh start, rather than enduring months of being chased by the debt recovery department before repossession inevitably occurs. What is interesting about voluntary foreclosure is that there are often no arrears, or the customer has only missed one or two payments. It occurs out of the blue with little or no warning. Voluntary foreclosure is a bit of a headache for mortgage providers. This is because homes that have been repossessed typically sell for well below market value. If someone is in arrears and foreclosure is a distinct possibility, then the mortgage company will account for the potential shortfall in their impairment charges (provisions for bad debts). However, when a home owner hands the keys to the property back to the lender without warning, the lender is faced with a sudden and unexpected loss, often tens and sometimes hundreds of thousands of dollars per property. What the bank wanted to do was use predictive analytics to forecast voluntary foreclosure within the next three months. Where the model predicted that voluntary foreclosure was likely, the bank would contact the customer and ask them to come in to their local branch for a face-to-face “financial review.” On the basis of the review the bank would do one of three things; If the customer was struggling, but the mortgage was potentially salvageable, then agree an action plan with the customer, before the arrears began. This might include rescheduling the debt, putting the customer in touch with debt charities, or even just providing a bit of moral support. If the situation looked hopeless, then help the customer to sell their home, while they were still in it. This would ensure that the full market price of the property would be realized, benefiting both the borrower and the lender. Nothing, if it appeared that the customer’s finances were sound (i.e. the model got it wrong). To meet the objective, the analytics team spent a couple of months gathering data and building what at first sight was a great model. The eventual show-stopper for this project arose when it came to implementing the model. Yes the model was very predictive in statistical terms, but what the business had lost sight of is that foreclosure is a rare thing and voluntary foreclosures even rarer. In any three month period, the bank typically foreclosed on about 1 in every 2,000 of the mortgages on its books, and of these, about a third were voluntary. So over a three month period an average of 1 in every 6,000 customers chose to hand back the keys to their home. As discussed, the model could identify cases that were 120 times more likely to enter voluntary foreclosure than average. This equates to a customer group where 1 in 50 (6,000 / 120) were likely to hand their keys back. The bank wanted to invite the people in this customer group to their local branch and review their situation, but for every 50 reviews undertaken, on average only 1 would be with someone who was likely to hand back their keys. The bank estimated that each review required about 2.5 hours to deal with. Therefore, about 120 hours (3 weeks) of staff time would be required to deal with just one case where voluntary repossession was likely. The cost of this action was far more than the bank was willing to accept and consequently the project was not taken forward.
3. Legal and reputational risks/issues that can arise when using predictive analytics.
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3. Legal and reputational risks/issues • The most prominent use of predictive
analytics is to predict the behaviour of individuals, using information that is known about them.
• Personal data is the key ingredient in the predictive analytics process when deriving models that predict individual behaviour.
• The general use (and misuse) of personal data is subject of increasing regulation in many regions
• If I follow all laws and regulations, then that’s all I need to worry about right? – This may prevent you being taken to
court, but won’t necessarily prevent bad publicity.
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Presenter
Presentation Notes
On a frequent basis, stories appear in the press relating to the use and misuse of data
3. Legal and reputational risks/issues
• One of the most significant problems, is the one of bias:
– +VE: A predictive model that has been designed correctly won’t display unjustified bias.
– -VE: The very nature of predictive models means that bias will exist. That’s why they work!
• But its “evidential bias” based on statistical evidence, so that’s OK?
• Maybe, but there are still situations where certain types of bias are illegal, and if you make decisions using the output of models that display these biases, then you may be breaking the law.
• Even when legal, the fact that a model displays certain biases can be controversial and may result in reputational damage, which more than outweighs any benefits brought by the model in terms of improved decision making.
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Presenter
Presentation Notes
Child illness exmaple age negatively corrolated with good outcomes.
3. Legal and reputational risks/issues • The easiest type of (illegal) bias to avoid is
direct bias; i.e. to ensure certain variables do not feature in certain types of model. For example gender, race, religion in credit scoring models. However, indirect bias can still exist.
• If income is included in a predictive model, then men and women will be treated differently, despite gender not explicitly being included.
• Likewise for occupation (e.g. Primary school teachers and Engineers).
• Things are also not universal. – Gender OK for marketing models, but not
for insurance models.
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Source: BBC How to assess the potential risk associated with using different data within a predictive model?
Presenter
Presentation Notes
EU law introduced a few years ago making it illegal, all other things being equal, for men and women to receive different insurance quoations
What data to use when?
• Age • Alcohol consumption • Credit history • Criminal records • Dependents • DNA • Driving speed • Education • Gas consumption • Gender • Grocery purchases at supermarket • Income
• Last book purchased • Live with smoker (Y/N) • Marital status • Medical history • Music currently listening too • Race • Religion • Sexual orientation • Smoker (Y/N) • Type of car you drive
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Presenter
Presentation Notes
What I’m now going to do is talk about a personal perspective, that I try and adopt when I’m dealing with data and decision making using predictive analytics, based on that data. This is something that I have thought about a lot over the years when working on various projects for a variety of public and private sector organizations. After thinking about the ethical angle, I then blend this with relevant legislation to come up with what I believe is ethical and satisfies relevant laws and regulations. So this is very much about my model of data, decision making and what’s ethical. So as I said at the beginning, you may agree with me, you may not, but I’ll be happy as long as it gives you something to take away and think about. So if I had some data items such as this, what would I do? How do I classify the data – what sort of lens would I apply when considering it?
Immutability of data
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Immutable (Individual can’t change at all)
Mutable (Individual can change easily)
Age
Alcohol consumption
Income Criminal record
Gas consumption
Education
Gender
Grocery purchases
Last book purchased
Live with smoker
Marital status
Medical history
Dependents Race
Religion Music currently Listening too
Sexual orientation
Smoker
Type of car
Driving speed
DNA
Presenter
Presentation Notes
One of the first things I think about is the immutability of the data at my disposal. How changeable is it? Is it something that the individual was born with and/or can’t change, or something that is very much down to life choices? Things like your DNA and ethnic origin are not things you can change at all. At the other end of the scale things like what books and CDs you buy are very much within your control. However its not all black and white. Immutability is more of a spectrum. There are lots of things that in theory an individual could change or do differently, but there are various huddles and difficulties to overcome. I can marry, divorce, marry again as often as I like, but there are social, financial and legal barriers that make it difficult to do this very often. �Likewise I don’t have to live with a smoker, but it’s a lot of effort to change that.
Beneficiary
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Individual / society Decision maker
Treatment for illness
Selection for tax inspection
Product marketing
Benefit payment
Foreclosure Match on dating site
Credit granting
Child protection Insurance pricing
For whose benefit is a decisions made? (This is not the same thing as if the individual benefits from the decision)
Suspect selection in criminal cases
Making job offers
Redundancy selection
Home improvement grants
Parole
Survey selection
Presenter
Presentation Notes
The second thing I think about is who is going to benefit from the decisions that are made, based upon the data I hold about them?. I look at this from the perspective of the decision maker (i.e. the user of the predictive model upon which the data is based) and the individual (about whom decisions are being made) This perspective varies greatly if you are working in public or private sector, and in what capacity decisions are being made.
Impact
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What is the potential impact of decisions on an individual’s well being?
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Low Impact High Impact
Treatment for illness
Selection for tax inspection
Product marketing
Benefit payment
Foreclosure Match on dating site
Credit granting
Child protection Insurance
pricing
Suspect selection in criminal cases
Making job offers
Redundancy selection
Home improvement grants
Parole
Survey selection
Presenter
Presentation Notes
The third angle I consider is the impact of the decision, and the magnitude of that decision individuals and how that will affect their well-being
Bringing it all together
Impact of decision on individual
Beneficiary of the decision
Immutability of data used
Ethical challenge
/ risk
High Decision maker
High Greatest
Least
Low Individual High
Low Low Decision
maker High Low
Individual High Low
• More legislation • Audit & regulatory oversight • Public interest • Greater manual involvement • Simple and explicable models • Judgemental overriding • Expert “Buy-in” • Understand model weaknesses • Constant monitoring
• Less legislation • Predictive ability trumps all else • Complex “black box” models • Automated model generation • Rapid redevelopment of models • Little oversight
E.G, foreclosure, redundancy,
parole
E.G. Marketing type
applications
Presenter
Presentation Notes
I’ve prioritised these three dimensions of data and decision making as shown here – you may have a different view The book within which this table appears actually puts the beneficiary first, but when I had a rethink when putting this presentation together. For example, if the impact of the decision is high and the decision is for someone other than the individual, and that decision is based on highly immutable data – then that is an ethically challenging decision to make. I’m not saying you can’t use highly immutable data in such circumstances, but you need to think very carefully about how defensible your position is. At the other end of the spectrum, if all I’m doing is target people who might be interested in using public facilities such as libraries and parks – then there is much less challenge to that sort of decision.
Appendix A: Sources of further information
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• Operational Database Management Systems. http:// www.odbms.org/ This site is supported by a range of industry experts. It covers a wide range of topics relating to the implementation and application of new technologies associated with predictive analytics, cloud computing and Big Data, amongst other things.
• KDnuggets. http://www.kdnuggets.com/ This is one of the most popular sites providing resources for data scientists.
• AnalyticBridge. http://www.analyticbridge.com/ AnalyticBridge hosts a range of articles, blogs and discussion forums about predictive analytics that is open to all. There is a broad range of topics covered, from the strategic to the very technical / operational.
• LinkedIn. http://www.linkedin.com/ There are several forums on LinkedIn that discuss predictive analytics and related topics.
• StatSoft. http://www.statsoft.com/Textbook. This is a website managed by Dell, providers of the STATISTICA statistical software package. If you want to know more about a wide range of statistical methods, including those used in predictive analytics, then this is a useful site to refer to.
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The following are some of the primary internet resources for PA and related technologies (Big data, data mining, machine learning, etc.)
Business / non-technical books • Davenport, T., Kim, J. (2013). Keeping Up with the Quants: Your Guide to
Understanding and Using Analytics. Harvard Business Review Press. Davenport was one of the first people to write an accessible analytics text in his 2006 book – Competing on Analytics. This new book is written specifically for non-technical managers to help them understand and work with technically minded people who do predictive analytics.
• Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods. Palgrave Macmillan. This is one of my books. Primarily it’s a book about predictive analytics, but it also provides a brief introduction to Big Data. The main focus is on practical issues around the development and implementation of predictive models.
• Siegel, E. (2016). Predictive Analytics: the Power to Predict Who Will Click, Buy, Lie, or Die. Wiley. 2nd Ed. Very much a marmite book. You’ll either love it or hate it, but it’s the book that brought predictive analytics to the attention of a much wider audience than ever before.
• Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail. Penguin. This is not really a predictive analytics book. However, what is relevant is the focus on understanding why so many forecasting systems fail. It discusses why more attention needs to focus on the weaknesses and pitfalls of forecasting and prediction, so as to improve the quality of forecasting models in the future.
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More academic / theory focused books • Baesens, B. (2014). Analytics in a Big Data World: The Essential Guide to Data
Science and its Applications. Wiley. This book describes the key stages involved in developing a predictive model. A good read for those with a little bit of mathematical and/or statistical knowledge, but you don’t need a higher degree in mathematics or statistics to understand the concepts that Baesens puts forward.
• Bishop, C. M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer. This book covers a lot of the theoretical material underpinning many of the tools commonly used for data mining and predictive analytics.
• Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Clarendon Press. It has been more than twenty years since its original publication, yet this remains one of the few definitive guides to the theory and application of neural networks.
• Hastie, T., Tibshirani, R. and Friedman, J. (2011). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer. A detailed and technical guide to many of the data mining tools used in predictive analytics, written by three of the world’s leading academics in the field.
• Hosmer, D. and Lemeshow, S. (2013). Applied Logistic Regression (Wiley Series in Probability and Statistics). 3rd Edition Wiley. Logistic regression remains one of the most popular and widely used methods for generating predictive models. This is the main book I recommend to people who want to know more about this method.
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More academic / theory focused books (Cont.) • Khun, M.(2013), Johnson, K. Applied Predictive Modeling. Springer. Another
well-constructed book in a similar vein to Baesens (above). It combines practical advice with the more mathematical aspects of the subject.
• Linoff, G. S. and Berry, M. J. (2011). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. 3rd. Edition. Wiley. This is a broad, well-rounded, and not overtly technical book that describes the most popular data mining techniques applied to direct marketing.
• Witten, I. H., Frank, E. and Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann. This is a detailed reference manual for those interested in practical data mining. I found it provided a nice blend of theory and practice, with many good examples.
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Bibliography
• Amatriain X and Basilico J. (2012). The NetFlix TechBlog: http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html
• Baraniuk, C. (8/9/2016). “LinkedIn denies gender bias claim over site search” BBC http://www.bbc.co.uk/news/technology-37306828
• BBC. (23/8/2016) “Gender pay gap: Why do mums increasingly earn less?” http://www.bbc.co.uk/news/business-37167610
• Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods. Palgrave Macmillan.
• Hand, D. J. (2006). Classifier technology and the illusion of progress. Statistical Science 21(1): 1-15.
• Segal, E. (2013). Predictive Analytics: the Power to Predict Who Will Click, Buy, Lie, or Die. Wiley.
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