Predicting Dropouts: How Regression Models Reveal Hidden Patterns in New York’s High Schools
A step-by-step data science journey from messy datasets to meaningful insights Quick Overview — What You’ll Learn Before we dive in, here’s what this article covers: Understanding the Problem: Predicting high school dropout rates — why it matters and how data can help. From Raw to Ready: Cleaning and preparing real-world education data for machine learning. Exploring the Story in the Data (EDA): How visualization uncovers trends and data integrity issues. Choosing the Right Model: Why “one-size-fits-all” doesn’t work for predicting counts like dropouts. Modeling in Action: Comparing Linear Regression, Poisson, and Negative Binomial models. Evaluating and Validating: How cross-validation ensures that your model isn’t just lucky. Lessons and Takeaways: What this project teaches us about modeling, data storytelling, and real-world decision-making. 1. Understanding the Problem Each year, educators and policymakers grapple with the same question: why...