Machine Learning 101

Transcription

1 Machine Learning 101 Mike Alperin September, 2016 Copyright TIBCO Software Inc.

2 Agenda What is Machine Learning? Decision Tree Models Customer Analytics Examples Manufacturing Examples Fraud Use Examples Machine Learning on the TIBCO Community Copyright TIBCO Software Inc.

3 What is Machine Learning? Copyright TIBCO Software Inc.

4 Machine Learning Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

5 Machine Learning Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Enabled by exponentially increasing compute power doubling every 2 years

6 Why use machine learning algorithms? Good Results Machine learning algorithms + Big Data sets can produce models that accurately fit complex data patterns. Can make predictions for complex processes & systems Can handle systems with hundreds or thousands of variables Easy to use / Simple user interface Computer algorithm does the heavy lifting Results presented with easy-to-understand visualizations Copyright TIBCO Software Inc. 6

7 Types of Machine Learning Supervised Solve known problems Build a model that predicts something What factors are driving fraud or customer behavior or manufacturing defects? Decision Trees, Random Forest, Gradient Boosting Machine Unsupervised Identify new patterns, Detect anomalies Are there new fraud clusters or buying patterns or failure modes emerging? Clustering, Principle Components, Neural Networks, Support Vector Machines Optimization Support Decision-making Find best solution even when there are complex constraints What is the optimum route to take or allocation of resources or equipment maintenance schedule? Genetic Algorithm Copyright TIBCO Software Inc.

8 Use Cases that leverage Machine Learning Customer Analytics - Prediction of customer behavior: customer segmentation, customer churn, cross-sell/up-sell, propensity Fraud & Financial crime Money laundering, credit card fraud, medical fraud, insurance fraud Manufacturing - Optimization of manufacturing equipment, processes and product yield Energy - Completions optimization, Blend optimization, Predictive maintenance Transportation & Logistics - routing optimization, fuel efficiency, predictive maintenance and warehouse distribution / space optimization Copyright TIBCO Software Inc.

9 Advanced Analytics and Big Data Tools Many more.

10 Decision Trees Copyright TIBCO Software Inc.

11 Decision Tree Titanic Survival Rate family size Wikipedia Copyright TIBCO Software Inc.

12 Classical Statistics Fit parameters to a well-defined model Copyright TIBCO Software Inc.

13 Decision Tree Product Pass / Fail by Process & Equipment Bad Product Good Product Peeling Clearcoat Automobile Paint Process < 132 C Clearcoat Bake Temperature >= 132 C 1, 2, 4 3 Basecoat Thickness Sanding Station

14 Decision Tree Training and Test Data Sets

15 Ensemble Tree Algorithms Random Forest, Gradient Boosting Machine (GBM) Method Average many simple trees Sample the data: fit a simple tree Re-sample the data; up-weighting the observations that weren t fitted well in previous model Continue adding trees until fit is good Save all the trees and average them Better fit + prediction than single trees Copyright TIBCO Software Inc.

16 Customer Analytics Examples Copyright TIBCO Software Inc.

17 Customer & Marketing Analytics Market Analytics Pricing Promotion Campaign Effectiveness Forecasting Market Mix Media Attribution Market (Syndicated) Data Store & Distribution Analytics Store Clustering; geospatial modeling Store Performance Forecasting Effects: Price, Promotion Distribution: Pick, Pack, Ship Store and DC Data Customer Acquisition Relationship Growth Customer Lifecycle Customer Retention Consumer Analytics Segmentation Propensity Affinity & Association Social: Sentiment & Intent Churn Loyalty Cross-sell / Up-sell Test & Learn (A B testing) Online Analytics (Path, Cart Abandonment, ) PoS, Panel Loyalty Data Copyright TIBCO Software Inc.

18 Customer Segmentation Top Shopper 27% of customers & 35% of revenues Broad purchase behavior Budget Minded 34% of customers & 29% of revenues Highly focused on core building categories Outdoor Plus 15% of customers & 16% of revenues Mainly outdoor, but other spending Gardener 10% of customers & 5% of revenues Primarily garden Seasonal Shopper 11% of customers & 12% of revenues Very event oriented Pool Customer 3% of customers & 4% of revenues Very focused on pool and patio categories Copyright TIBCO Software Inc.

19 Segmentation - Cluster Analysis

20 Propensity to Buy Customer Success Story Objectives: Select most important Response Products to highlight in 2015 Holiday season direct marketing Identify and quantify predictive significance of Driver Products based on historical data from 2014 sales Build campaigns for as many people as possible that are relevant

21 Propensity to buy models

22 Results Same year repeat visits are 3x higher for customers targeted in the campaign Average order value is much higher Year over Year repeat visitors is double

23 Telco Machine Learning Churn Model

24 Attrition and Value Models Predicted Prob(attrition) = f (X, b) Y variable Attrition (Y/N over time period) X variables How long a member Website interactions - section Prior spend Time since last interaction Experian: demog, f function Additive Model Random Forest, Gradient Boosting Variable Names Redacted

25 Call Center Real-time Alert Actions

26 Real-Time Customer Interactions / Offers No Login = No Customer History => Offer based on Product Association Sarah Login = Sarah s Customer History => Offer based on Propensity Model scored for Sarah Copyright TIBCO Software Inc.

27 Manufacturing Examples Copyright TIBCO Software Inc.

28 Correlate Product or Equipment Results to Process & Supplier Data Supplier - Incoming Materials and Components measured electrical, chemical, physical characteristics batch-id, lot_id Manufacturing Process Physical, chemical or electrical measurements WIP / MES: track-in / track-out date, process equipment id, recipe, operator, Process equipment sensor data Equipment Maintenance logs Defect Inspections Cost of labor, materials, machines and facilities Product Quality and Reliability Test Measured product functional and performance characteristics Accelerated life test results Product Field Returns Failure mode, unit / batch / lot ID Failure analysis root cause results Warranty / Repair claim, call center and cost structured & unstructured

29 Machine Learning to Predict Equipment or Product Fails Problem Value Method Product & Equipment problems difficult to accurately diagnose for complex manufacturing processes Big Data problem millions of units, hundreds / thousands of predictors Response: Product, Process or Equipment Fail data Predictors: in-process equipment, process and product measurements or attributes Being used by customers to find previously undetected problems. Reduces time-tomarket and increases profit. GBM analysis template to identify significant predictors, interactions and nonlinearities For large datasets, hybrid data access used to perform variable reduction step in-db Simple interface easy for business analyst to run and interpret results GBM results for semiconductor yield as a function of in-process equipment & product measurements

30 Real-time Predictive Analytics for Process Cost reduction Goal: Scrap parts as early as possible to reduce costs in a manufacturing process. Question: When to scrap a part in Station 1 instead of sending it to Station 2? Station 1 Station 2 Cost Before Total Cost 29 (or more) Scrap? Scrap?

31 TIBCO Spotfire with H2O Integration Advanced Analytics ( Scrap parts as early as possible! )

32 Deploy real-time model: TIBCO Live Datamart & Streambase Operational Intelligence ( Monitor the manufacturing process and change rules in real time! ) Live Dartmart Desktop Client

33 Fraud Examples Copyright TIBCO Software Inc.

34 Step 1 Catching New Fraud Like Old Fraud Supervised Learning Model to predict credit card fraud based on customer information: Variable Importance chart

35 Sort existing transaction by Probability of Fraud

36 Step 2 - Find unusual transactions - Unsupervised learning Fradulent Good Algorithm examples: Principle Component Analysis Auto-encoder Neural Network Single-class Support Vector Machine Clustering (e.g. K-means, Hierarchical)

37 Sort existing transaction by Oddity Prioritize investigators work

38 Step 3 apply models in real-time with Streambase Deploy models in real-time with a click from Spotfire

39 Monitor transactions in real time with LiveView

40 Machine Learning on the TIBCO Community Copyright TIBCO Software Inc.

41 Learn & Do More: Machine Learning on the TIBCO Community Wiki page Component Exchange: Data functions Accelerators Templates Copyright TIBCO Software Inc.

42 Thank You Copyright TIBCO Software Inc.