Modeling Zero-Inflated Data: What Every Data Scientist Should Know
Modeling Zero-Inflated Data: What Every Data Scientist Should Know Imagine you’re working on a machine learning project predicting customer purchases. You find that a large portion of your data contains zeros — no purchase was made. When you train a model, it performs poorly. What went wrong? Zero inflated modeling Welcome to the world of zero-inflated datasets — a common and often overlooked problem in data science. In this article, you’ll learn: What zero-inflated data is Why standard models fail How to correctly model zero-inflated data A working Python example to bring it all together What is Zero-Inflated Data? Zero-inflated data refers to datasets where the response variable contains an excess of zeros , often more than expected under common statistical distributions like Normal or Poisson. Common Scenarios: E-commerce: Users with zero purchases Insurance: Claims with zero payouts Healthcare: Patients with no readmissions Advertising: Campaigns with no conversi...
