DM1: Introduction: Machine Learning and Data Mining - Sorry

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Assessing Credit Risk: Case Study • Situation: Person applies for a loan • Task: Should a bank approve the loan? • Note: People who have the best credit don’t need the loans, <strong>and</strong> people with worst credit are not likely to repay. Bank’s best customers are in the middle 18
Credit Risk - Results • Banks develop credit models using variety of machine learning methods. • Mortgage <strong>and</strong> credit card proliferation are the results of being able to successfully predict if a person is likely to default on a loan • Widely deployed in many countries 19
Page 1 and 2: Machine Learning, Data Mining, and
Page 3 and 4: Lesson Outline •Introduction: Dat
Page 5 and 6: Big Data Examples • Europe's Very
Page 7 and 8: From terabytes to exabytes to …
Page 9 and 10: Data Growth In 2 years, the size of
Page 13 and 14: Application Areas What do you think
Page 15 and 16: Customer Attrition: Case Study •S
Page 17: Customer Attrition Results • Veri
Page 21 and 22: Successful e-commerce - Case Study
Page 23 and 24: Genomic Microarrays - Case Study Gi
Page 25 and 26: Security and Fraud Detection - Case
Page 29 and 30: Related Fields Machine Learning Vis
Page 31 and 32: Knowledge Discovery Process flow, a
Page 35 and 36: Data Mining Tasks: Classification L
Page 37 and 38: Summary: • Technology trends lead
Page 39: 1997 1998 1999 2000 2001 2002 2003

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