Prescriptive analytics

Introduction to Data Literacy

Anneleen Rummens

Data Scientist

Analytics overview

Analytics overview

Introduction to Data Literacy

Why use prescriptive analytics?

  • Make informed, data-driven decisions
  • Optimize processes
  • Mitigate risks

Zoom-in of prescriptive analytics

Introduction to Data Literacy

Common techniques

  • Rule-based systems
  • Reinforcement learning
  • Scenario and simulation analysis
  • Recommendation engine

Example of a rule-based system

Introduction to Data Literacy

Recommendation engine

  • Predicts interests based on past behavior
  • Provides recommendations based on predicted interests

Examples of recommendation engines

Introduction to Data Literacy

Case study: fraud detection

A set of transactions on an account has been flagged as possibly fraudulent.

How should we proceed?

  • Generate rules to select best action
  • Simulate estimated losses with each scenario

Use insights to help make informed decision

Introduction to Data Literacy

Let's practice!

Introduction to Data Literacy

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