Neural Networks Automatic Model Building (Machine Learning) Artificial Intelligence

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-2 High-Growth Product Used for classifying data –target customers –bank loan approval –hiring –stock purchase –trading electricity –DATA MINING Used for prediction

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-3 Description Use network of connected nodes (in layers) Network connects input, output (categorical) –inputs like independent variable values in regression –outputs: {buy, don’t} {paid, didn’t} {red, green, blue, purple} {character recognition - alphabetic characters}

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-4 Network InputHiddenOutput LayerLayersLayer Good Bad

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-5 Operation Randomly generate weights on model –based on brain neurons input electrical charge transformed by neuron passed on to another neuron –weight input values, pass on to next layer –predict which of the categorical output is true Measure fit –fine tune around best fit

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-6 Operation Useful for PATTERN RECOGNITION Can sometimes substitute for REGRESSION –works better than regression if relationships nonlinear –MAJOR RELATIVE ADVANTAGE OF NEURAL NETWORKS: YOU DON’T HAVE TO UNDERSTAND THE MODEL

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-7 Neural Network Testing Usually train on part of available data –package tries weights until it successfully categorizes a selected proportion of the training data When trained, test model on part of data –if given proportion successfully categorized, quits –if not, works some more to get better fit The “model” is internal to the package Model can be applied to new data

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-8 Business Application Best in classifying data mortgage underwritingasset allocation bond ratingfraud prevention commodity trading Predicting interest rate, inventory firm failurebank failure takeover vulnerabilitystock price corporate merger profitability

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-9 Neural Network Process 1.Collect data 2.Separate into training, test sets 3.Transform data to appropriate units Categorical works better, but not necessary 4.Select, train, & test the network Can set number of hidden layers Can set number of nodes per layer A number of algorithmic options 5.Apply (need to use system on which built)

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-10 Marketing Applications Direct marketing –database of prospective customers age, sex, income, occupation, education, location predict positive response to mail solicitations THIS IS HOW DATA MINING CAN BE USED IN MICROMARKETING

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-11 Neural Nets to Predict Bankruptcy Wilson & Sharda (1994) Monitor firm financial performance Useful to identify internal problems, investment evaluation, auditing Predict bankruptcy - multivariate discriminant analysis of financial ratios (develop formula of weights over independent variables) Neural network - inputs were 5 financial ratios - data from Moody’s Industrial Manuals (129 firms, ; 65 went bankrupt) Tested against discriminant analysis Neural network significantly better

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-12 Ranking Neural Network Wilson (1994) Decision problem - ranking candidates for position, computer systems, etc. INPUT - manager’s ranking of alternatives Real decision - hire 2 sales people from 15 applicants Each applicant scored by manager Neural network took scores, rank ordered best fit to manager of alternatives compared (AHP)

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-13 CASE: Support CRM Drew et al. (2001), Journal of Service Research Identify customers to target Customer hazard function: –Likelihood of leaving to a competitor (CHURN) Gain in Lifetime Value (GLTV) –NPV: weight EV by prob{staying} –GLTV: quantified potential financial effects of company actions to retain customers

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-14 Models: Proportional Hazards Regression Neural Networks –Estimate hazard functions Baseline Regression Models –Models for longitudinal analysis

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-15 Data: Data Warehouse of Cellular Telephone Division Billing –Previous balance, access charges, minutes used, toll charges, roaming charges, optional features Usage –Number of calls, minutes by local, toll, peak, off-peak Subscription –Months in service, rate plan, contract type, date, duration Churn –Binary flag Demographics –Age, profitability to firm (current & future)

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-16 Model Use Sample of 21,500 subscribers, April 1998 Modeled tenure for 1 to 36 months Trained on 15,000 of these samples –Remainder used for testing Neural network models worked better than traditional statistics

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-17 Systems A great many products general NN products $59 to components DATA MINING along with megadatabasesother products library callable specialty products construction bidding, stock trading, electricity trading

McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 7-18 Potential Value THEY BUILD THEMSELVES –humans pick the data, variables, set test limits CAN DEAL WITH FAST-MOVING SITUATIONS –stock market CAN DEAL WITH MASSIVE DATA –data mining Problem - speed unpredictable