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.…