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Neural Networks

Neural Networks. Automatic Model Building (Machine Learning) Artificial Intelligence. High-Growth Product. Used for classifying data target customers bank loan approval hiring stock purchase trading electricity DATA MINING Used for prediction. Description.

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Neural Networks

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  1. Neural Networks Automatic Model Building (Machine Learning) Artificial Intelligence

  2. High-Growth Product • Used for classifying data • target customers • bank loan approval • hiring • stock purchase • trading electricity • DATA MINING • Used for prediction

  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}

  4. Network Input Hidden Output Layer Layers Layer Good Bad

  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

  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

  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

  8. Business Application • Best in classifying data mortgage underwriting asset allocation bond rating fraud prevention commodity trading • Predicting interest rate, inventory firm failure bank failure takeover vulnerability stock price corporate merger profitability

  9. Neural Network Process • Collect data • Separate into training, test sets • Transform data to appropriate units • Categorical works better, but not necessary • Select, train, & test the network • Can set number of hidden layers • Can set number of nodes per layer • A number of algorithmic options • Apply (need to use system on which built)

  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

  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, 1975-1982; 65 went bankrupt) Tested against discriminant analysis Neural network significantly better

  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)

  13. CASE: Support CRMDrew 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

  14. Models: • Proportional Hazards Regression • Neural Networks • Estimate hazard functions • Baseline Regression Models • Models for longitudinal analysis

  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)

  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

  17. Systems A great many products • general NN products $59 to $2,000 @Brain BrainMaker Discover-It • components DATA MINING along with megadatabases other products • library callable • specialty products construction bidding, stock trading, electricity trading

  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

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