Predictive Analytics Lecture Notes

Host Introduction

• Host: Eric Wilson
• Podcast: IBF On Demand
• Sponsor: Arkiva - SNOP Software Solution
• Encouragement to like, share, and subscribe to grow the community.

Topic Overview

• Discussion on predictive analytics: definition and importance.
• Special guest: Eric Siegel, expert in predictive analytics and machine learning.

What is Predictive Analytics?

• Definition: Applications of machine learning for business problems.
• Differences from forecasting:
  • Forecasting: singular predictions (e.g., quarterly sales).
  • Predictive Analytics: generates predictive scores for individuals or entities (e.g., customers, products).
• Impacts areas of business such as marketing, fraud detection, and credit risk management.

Importance and Evolution of Predictive Analytics

• Represents the latest evolution in information technology.
• Moves from big data management to applications of science to learn from data.
• Predicting per individual offers actionable insights for large-scale operations.

Key Concepts

• Data: Collection of historical outcomes used for learning.
• Big Data Myth: It’s not just about volume, but also about the variety and value of data.
• Training Data: Historical examples used for learning and making predictions.
• Modeling Methods: Includes decision trees, logistic regression, and ensemble models.

Machine Learning Fundamentals

• Overfitting: When the model learns noise in the training data rather than the signal.
• Testing Models: Essential to validate models using a test set that wasn't part of the training data.

Ensemble Modeling

• Combines multiple simple models to improve prediction accuracy (akin to the wisdom of crowds).
• Reduces brittleness of individual models by averaging or voting.

Uplift Modeling

• Also known as persuasion modeling; predicts how likely an individual is to respond positively to a treatment (e.g., marketing).
• Requires control groups to assess the impact of interventions.

Getting Started in Predictive Analytics

• Recommendations for those new to the field:
  • Take courses (like the Coursera course "Machine Learning for Everyone") to understand both methods and organizational processes.
  • Focus on both the technical side and the practical application of predictive analytics.

Conclusion

• Summary of Predictive Analytics: A process using statistical algorithms to detect future patterns.
• Key characteristics:
  1. More and different data (external drivers).
  2. More and different models (machine learning methods).
  3. More forward-looking predictions.
• Encouragement to engage with resources (books, courses) to further understanding.

Final Thoughts

• Host encourages ongoing learning in predictive analytics and shares contact information for further engagement.
• Reminder to wash hands for health safety.