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Maximizing Analytical Models: A Data Scientist's Guide

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Presentation Transcript

  1. Analytics

  2. Introduction • Analytics Process Model • Example Analytics Applications • Data Scientist Job Profile • Data Preprocessing • Types of Analytics • Post Processing of Analytical Models • Critical Success Factors for Analytical Models • Economic Perspective On Analytics • Improving the ROI of Analytics • Privacy and Security

  3. Analytics Process Model

  4. Example Analytics Applications • Risk analytics • credit scoring • fraud detection • Marketing analytics • churn prediction • response modeling • customer segmentation • Recommender systems • Texts analytics

  5. Example Analytics Applications

  6. Data Scientist Job Profile • Statistics, machine learning and/or quantitative modeling • Programming • Communication/Visualization • Business Knowledge • Creativity

  7. Data Preprocessing • Denormalizing data for analysis • Sampling • Exploratory Analysis • Missing values • Outlier Detection and Handing

  8. Denormalizing data for analysis

  9. Sampling • Take a subset of historical data to build analytical model • Good sample should be representative for the future entities on which the analytical model will be run • Choosing the optimal time window of the sample involves a trade-off between lots of data and recent data

  10. Exploratory Analysis

  11. Exploratory Analysis • Descriptive statistics • Mean • Median • Mode • Standard deviation • Percentile values

  12. Missing Values

  13. Missing Values • Keep • Delete (observation or variable) • Replace (aka impute)

  14. Outlier Detection and Handling • Valid versus invalid observations • Outlier detection • Minimum/Maximum • Histogram, box plot, scatter plot • Outlier handling • Treat as missing value (invalid observation) • Capping (valid observation)

  15. Types of Analytics • Predictive Analytics • Evaluating Predictive Models • Descriptive Analytics • Social Network Analytics

  16. Predictive Analytics • Linear Regression • Logistic Regression • Decision Trees • Other predictive analytics techniques

  17. Linear Regression

  18. Linear Regression

  19. Logistic Regression

  20. Logistic Regression

  21. Logistic Regression

  22. Logistic Regression

  23. Decision Trees

  24. Decision Trees • Splitting decision • Which variable to split at what value • Stopping decision • When to stop adding nodes to the tree? • Assignment decision • What class to assign to a leaf node?

  25. Decision Trees • Entropy: E(S) = -pGlog2(pG)-pBlog2(pB) (C4.5/See5) • Gini: Gini(S) = 2pGpB (CART)

  26. Decision Trees

  27. Decision Trees • Entropy top node = -1/2 x log2(1/2) – 1/2 x log2(1/2) = 1 • Entropy left node = -1/3 x log2(1/3) – 2/3 x log2(2/3) = 0.91 • Entropy right node = -1 x log2(1) – 0 x log2(0) = 0 • Gain = 1 – (600/800) x 0.91 – (200/800) x 0 = 0.32

  28. Decision Trees

  29. Decision Trees

  30. Decision Trees • Regression trees

  31. Decision Trees

  32. Other predictive analytics techniques • Ensemble methods • Bagging, Boosting, Random Forests • Neural Networks • Support Vector Machines • Deep Learning • Trade-off between model performance and interpretability!

  33. Evaluating Predictive Models • Splitting up the data set • Performance Measures for Classification Models • Performance Measures for Regression Models • Other Performance Measures for Predictive Analytical Models

  34. Splitting up the data set TRAIN/TEST DATA

  35. Splitting up the data set CROSS-VALIDATION

  36. Splitting up the data set BOOTSTRAPPING

  37. Performance Measures for Classification Models

  38. Performance Measures for Classification Models • Classification accuracy = (TP+TN)/(TP+FP+FN+TN) = 3/5 • Classification error = (FP +FN)/(TP+FP+FN+TN) = 2/5 • Sensitivity = Recall = Hit rate = TP/(TP+FN) = 1/2 • Specificity = TN/(FP+TN) = 2/3 • Precision = TP/(TP+FP) = 1/2 • F-measure = 2 * (Precision * Recall)/(Precision +Recall) = 1/2

  39. Performance Measures for Classification Models AUC represents probability that randomly chosen churner gets higher score than randomly chosen non-churner!

  40. Performance Measures for Classification Models

  41. Performance Measures for Classification Models Note: AUC : AR=2*AUC-1

  42. Performance Measures for Classification Models

  43. Performance Measures for Regression Models

  44. Performance Measures for Regression Models

  45. Other Performance Measures for Predictive Analytical Models • Comprehensibility • Justifiability • Operational efficiency

  46. Descriptive Analytics • Association rules • Sequence rules • Clustering

  47. Association Rules

  48. Association Rules

  49. Association Rules • Post processing • Filter out trivial rules • Sensitivity analysis • Visualization • Measure economic impact

  50. Sequence Rules Session 1: A, B, CSession 2: B, CSession 3: A, C, D Session 4: A, B, D Session 5: D, C, A Calculate confidence and support as with association rules!

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