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Ista2017 making sense of big data
- 2. 16 – 17 November, SofiaISTACON.ORG Making Sense of Big Data through machine learning and statistical modelling Dimiter Shalvardjiev Code Runners
- 3. 16 – 17 November, SofiaISTACON.ORG /about • Background in Information Security & Marketing • Started a consultancy in 2012 – Code Runners • Built analytical / ML products for Philips, Stryker, Pfizer, Chanel, Coca-Cola
- 4. 16 – 17 November, SofiaISTACON.ORG ML 101 The “what” in WTF
- 5. 16 – 17 November, SofiaISTACON.ORG Whatever happened to big data
- 6. 16 – 17 November, SofiaISTACON.ORG Machine learning vs. dynamic correlation • “Correlation does not imply causation” • Dynamically build customer segments • Optimize outcomes semi-unsupervised • Reinforcement learning • Probabilities are more efficient than stricter rules over larger datasets
- 7. 16 – 17 November, SofiaISTACON.ORG ML fundamentals Data Type labelled unlabelled mixed Machine Learning Types supervised unsupervised semi-supervised Approach reinforced regression classification clustering association decision tree neural net d-reduction
- 8. 16 – 17 November, SofiaISTACON.ORG ML examples Housing Price labelled supervised Medical IR* labelled supervised Customer Segmentation unsupervised unlabelled Fraud Detection mixed clusteringregression classification semi-supervised classification Big Brother mixed reinforced ensemble
- 9. 16 – 17 November, SofiaISTACON.ORG deep learn ML algorithms neural nets regression linear logistic stepwise MARS LOESS perceptron back-prop Hopfield net RBFN Boltzmann Deep Belief convolutional autoencoder clustering k-means k-medians stepwise EM hierarchical ensemble d-reduction decision tree association instance- based Bayesian
- 10. 16 – 17 November, SofiaISTACON.ORG A Real-Life Problem How to maximize sales
- 11. 16 – 17 November, SofiaISTACON.ORG “"The best minds of my generation are thinking about how to make people click ads." Jeff Hammerbacher, Cloudera
- 12. 16 – 17 November, SofiaISTACON.ORG Consultative Selling • Suggesting the best available option for your current need • dependent on proper need identification / guess • available to search vendors / large social networks / retailers • Upselling through suggestions, based on historic behavior • purchases – supplementary products • interests – what have you been looking at • segmentation analysis
- 13. 16 – 17 November, SofiaISTACON.ORG Consultative Selling
- 14. 16 – 17 November, SofiaISTACON.ORG • Online shopping platform • 1 000 000+ products with ~50 important attributes, ~250 attributes total • 25 000+ unique daily purchases with more than 1 product Scenario
- 15. 16 – 17 November, SofiaISTACON.ORG • Study consumer behavioral patterns • Segment users • a mix of behavioral & demographic factors • weigh in channel specifics • Improve suggestions over time, including last-minute purchase opportunities Goal
- 16. 16 – 17 November, SofiaISTACON.ORG Architecture of a (possible) reinforcement solution
- 17. 16 – 17 November, SofiaISTACON.ORG Machine learning: why?
- 18. 16 – 17 November, SofiaISTACON.ORG (Big) Data component (Streaming) Machine Learning
- 19. 16 – 17 November, SofiaISTACON.ORG Machine learning: how? Datasets
- 20. 16 – 17 November, SofiaISTACON.ORG Inside Azure ML
- 21. 16 – 17 November, SofiaISTACON.ORG A few shortcomings what not to do
- 22. 16 – 17 November, SofiaISTACON.ORG Limitations of ML adoption
- 23. 16 – 17 November, SofiaISTACON.ORG Limitations of ML adoption • Product pricing (not promotions!) • Perceived unfairness / bias by your customers, e.g. Expedia • Customer re-targeting via segmentation techniques • Mismanaged expectations • Wider cross-segment suggestions • Perceived violation of personal space may lead to churn
- 24. 16 – 17 November, SofiaISTACON.ORG Improvement suggestions what we know might be a good idea
- 25. 16 – 17 November, SofiaISTACON.ORG Thank you! @d1sh4 www.code-runners.com dshalvardjiev dimiter.shalvardjiev@code-runners.com