scaling techniques

Scaling techniques are methods used to adjust data so that different datasets or features can be compared and processed effectively. In simple terms, they transform data to have consistent ranges or distributions. Common methods include normalization, which resizes data to fit within a specific range (like 0 to 1), and standardization, which adjusts data to have a consistent mean and variance. These techniques help machine learning models perform better by ensuring that no single feature dominates because of its scale, leading to more accurate and efficient analysis.