The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations

Episode 3 of The Data Science Podcast dives into a real-world case study: how a $12 billion regional bank in the Midwest overhauled its consumer credit scoring model using ensemble methods. Lucas walks through the specific problem — a legacy logistic regression that was rejecting too many thin-file applicants — and how a gradient-boosted tree ensemble, combined with a neural network meta-learner,...

In this episode of The Data Science Podcast, Lucas and Luna dive into one of the most common yet overlooked pitfalls in machine learning: data leakage. They break down how a seemingly innocent preprocessing misstep can cause models to appear 20% more accurate in training only to collapse in production. Using the real-world example of a medical diagnosis model that flagged patient IDs as a top pred...

On the premiere of The Data Science Podcast with Fexingo, Lucas and Luna anchor on a startling fact: 87 percent of machine learning projects never make it to production—and of those that do, nearly half degrade within the first six months. They drill into one specific metric—prediction drift—using a case from a mid-sized e-commerce company whose recommendation engine started recommending winter co...