Predictive Analytics books

[former_member203645]

Hi all,

To get started and understanding the basics, could some one recommend me books on Predictive Analytics. For now I don't need technical concepts.

I want to know what algorithms & .... to use and for what & how in functional way.

http://www.amazon.com/s/ref=nb_sb_ss_i_0_12/189-9858310-0951520?url=search-alias%3Daps&field-keyword...

[henrique_pinto]

I'd suggest the text books of some high profile college course on Data Science/Statistics.

For example, this series of videos of a Google Employee who also taught the STAT202 course on Stanford points to this book: http://www.amazon.com/Introduction-Data-Mining-Pang-Ning-Tan/dp/0321321367

(BTW there will be a newer release in November: http://www.amazon.com/Introduction-Data-Mining-Pang-Ning-Tan/dp/0133128903)

Also, check the list of recommended readings of data science/statistics related courses in MOOC platforms, such as Coursera:

https://class.coursera.org/datasci-001/wiki/view?page=syllabus

(you might need to be logged on Coursera to be able to see its content)

Best,

Henrique.

[michael_laux]

Hello RUC,

for what you describe I guess the Book: "Data Mining and Knowledge Discovery Handbook"

http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-0-387-09822-... is a good "overview". If you are looking for a theoretical Basis you could have a look at

the pdf-available Book "Elements of Statistical Learning" http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Thank you,

Michael

[Former Member]

I work and write books in the predictive analytics space (and have a doctorate in the subject) and this is my recommended reading list(its part of an appendix in one of my forthcoming books). The ones in the "Middle ground" are probably what you are looking for and will describe the various algorithms available without too much theory.

Non-technical / introductory books about predictive
analytics that have no/very little technical content.

Siegel, E. (2013). Predictive Analytics: the Power to Predict
Who Will Click, Buy, Lie, or Die. Wiley. This won't tell you how to do predictive analytics but is great for those new to the subject as a taster.

Silver, N. (2012). The Signal and the Noise: Why So Many
Predictions Fail. Penguin.  Not specifically about predictive analytics or Big Data, there is a lot of wisdom here that anyone dealing with predictive analytics can learn from.

Kahneman, D. (2012). Thinking, Fast and Slow. Penguin. This is a great book about how people think when making decisions. Again not a PA book as such but very interesting.

The middle ground.

These books have some specialist content but not an overwhelming amount. Even if you avoid the technical stuff, then there is probably still a good deal that you can get from these books.

Linoff, G. S. and Berry, M. J. (2011). Data Mining Techniques: For Marketing,
Sales, and Customer Relationship Management. 3^rd. Edition.
Wiley. Somebody has already recommended this on this post and I agree. Its a broad, well-rounded, and not overtly technical book, that describes the most popular data mining techniques applied to direct marketing.

Finlay, S. (2012). Credit Scoring, Response Modeling and Insurance Rating. 2^nd
Edition. Palgrave Macmillan. This is my book, with a focus on financial services.

Ian H. Witten, I. H., Frank, E. and Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 3^rd Edition (The Morgan Kaufmann Series in Data Management Systems). 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.

Menard, S. (2002). Applied Logistic Regression Analysis.2^nd Edition. Sage. This provides a short practical guide to using logistic regression. There are many newer algorithms that are theoretically superior to logistic regression when it comes to constructing classification
models. However, logistic regression remains the most popular method employed
in practice, and in practice often performs as well as more theoretically appropriate approaches such as neural networks and support vector machines.

1.2      Academic/Technical books

My recommendation is that you’d benefit from a good grounding in maths or statistics if you want to tackle the books in this section.

Hosmer, D. and Lemeshow, S. (2013). Applied Logistic Regression (Wiley Series in Probability and Statistics). 3^rd Edition Wiley. This book provides a detailed look at the theory and application of Logistic regression, which remains the most widely applied method for generating binary classification models.

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Clarendon Press. This is one of the few definitive guides to the theory and application of neural networks.

Hastie, T., Tibshirani, R. and Friedman, J. ()  The Elements of Statistical Learning: Data
Mining, Inference, and Prediction, 2^nd Edition. Springer. This is a heavy weight guide to many of the data mining tools used in predictive analytics.

Bishop, C. M. (2007). Pattern Recognition and Machine Learning
(Information Science and Statistics). Springer.  This book covers a lot of the theoretical
material for the subject.

[former_member203645]

Thanks a lot

[Boris]

Hello RUC,

maybe this Link can help you for the beginning.

http://scn.sap.com/docs/DOC-43307

For more details you should read some books about Data-Mining and multivariate statistics.

greetings

boris

[former_member203645]

Thanks for the input

Do you recommend any specific books in particular ??

[Former Member]

I started learning about PA with

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Gordon S. Linoff and Michael J. Berry

[Former Member]

I am reading and particularly enjoy An Introduction to Statistical Learning with applications in R
by James, Witten, Hastie and Tibshirani. The book is available for free via pdf at http://www-bcf.usc.edu/~gareth/ISL/index.html with permission of the authors and publisher. It is a great introduction to many predictive models that is a bit easier math-wise than Elements of Statistical Learning that was mentioned in a different post. They are both excellent books. ISLR has great R labs at the end of each chapter.

Have fun,

JD