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Feature scaling is needed because raw features often differ in units and magnitudes, making them incomparable. Scaling brings them onto a common scale, which is necessary for many machine learning algorithms to function properly.

Conditions: Dataset contains multiple features with different units or ranges.; The model is sensitive to feature magnitudes (e.g., distance-based or gradient-based).

Feature scaling is needed because raw features often differ in units and magnitudes, making them incomparable. Scaling brings them onto a common scale, which is necessary for many machine learning algorithms to function properly.

Conditions: Dataset contains multiple features with different units or ranges.; The model is sensitive to feature magnitudes (e.g., distance-based or gradient-based).

Min-max normalization transforms a feature value $X$ into $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$, mapping the feature's observed range onto the interval $[0, 1]$.

Conditions: Applied per numerical feature.; Requires a finite minimum $X_{\min}$ and maximum $X_{\max}$ for the feature.; Assumes $X_{\max} \neq X_{min}$ to avoid division by zero.

Min-max normalization transforms a feature value $X$ into $X_{\text{norm}} = \frac{X - X_{\min}}{X_{\max} - X_{\min}}$, mapping the feature's observed range onto the interval $[0, 1]$.

Conditions: Applied per numerical feature.; Requires a finite minimum $X_{\min}$ and maximum $X_{\max}$ for the feature.; Assumes $X_{\max} \neq X_{min}$ to avoid division by zero.