DM1: Introduction: Machine Learning and Data Mining - Sorry

More documents

Recommendations

Info

Customer Attrition: Case Study Task: •Predict who is likely to attrite next month. •Estimate customer value <strong>and</strong> what is the cost-effective offer to be made to this customer. 16
Customer Attrition Results • Verizon Wireless built a customer data warehouse • Identified potential attriters • Developed multiple, regional models • Targeted customers with high propensity to accept the offer • Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers) (Reported in 2003) 17
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: Customer Attrition: Case Study •S
Page 19 and 20: Credit Risk - Results • Banks dev
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

DM1: Introduction: Machine Learning and Data Mining - Sorry

Create successful ePaper yourself

Delete template?

Save as template?