A beginner-friendly guide to one of the sneakiest mistakes in machine learning Introduction Imagine you build a machine learning model, test it, and get an amazing 99% accuracy. You’re thrilled until you deploy it in the real world and it performs terribly. What went wrong? In many cases, the answer is data leakage one of the most common and most dangerous mistakes in data science. It’s often called a “hidden trap” because everything looks perfect during training and testing, but the model secretly cheated and won’t work on new, unseen data. In this post, we’ll break down what data leakage is, why it happens, how to spot it, and how to prevent it all explained in simple terms for beginners. What Is Data Leakage? Data leakage happens when information from outside the training dataset — information that wouldn’t be available at prediction time in real life — accidentally gets used to train your model. In simple words: your model gets a sneak peek at the “answer” durin...