Beyond Big Data Gathering: Creating Value from Feature Engineering

Gone are the days where gathering and organizing vast lakes of data was enough to create value for organizations. Financial institutions in particular have captured significant amounts of data for years but are failing to truly use this asset in ways that deliver value for their organizations and customers.

Looking Past the Algorithm

Best Practices for Feature Engineering

  • Calculate “Estimated Value” for a home using an average of “Comparable Sales” by “Square Footage”
  • Produce DTI by calculating ratio of “Credit Payments” to “Current Income”
  • Derive a “Retirement Gap” by calculating the “Future Value” of existing assets and comparing to “Current Income”
  • Scaling values between min-max of a variable such as age in the dataset into a range of [0, 1]
  • Examining the number of purchases in particular types of retail stores as an indicator of “interest” in certain consumer goods
  • Principal Component Analysis (PCA) and Independent Component Analysis (ICA) map existing data to another feature space
  • Deep Feature Synthesis (DFS) allows for transfer of intermediate learnings from middle layers in the Neural Networks

Key Steps for Success

Conclusion

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