You've tried three different algorithms. None of them break 78% accuracy. You add dropout, tune hyperparameters, try XGBoost. Still stuck. Then you create one new feature from the existing data. Accuracy jumps to 86%. That's feature engineering. And it's the part of ML that makes the biggest difference in practice. Not the algorithm. Not the hyperparameters. The features. This post covers the core techniques you'll actually use on real datasets. What You'll Learn Here Why features matter more than algorithms Handling categorical variables: label encoding vs one-hot encoding Scaling and transformation: when and why Creating new features from existing ones Interaction features and polynomial features Handling dates and times Domain-specific feature ideas Feature selection: dropping what doesn't help Why Features Beat Algorithms Here's a concrete example. You're predicting house prices.…