Min-Max Normalization scales data to a fixed range, preserving shape but sensitive to outliers, while Z-score Standardization centers data with mean 0 and std dev 1 using (X - ) / , making it robust to outliers and useful for algorithms assuming normal distribution. Both transform values, but Min-Max is bounded, whereas Z-score indicates distance from the mean in standard deviations. This method linearly scales features to a specific range, typically [0]. Formula is (X_norm = (X - X_min) / Xmax - Xmin). It's computed by finding the minimum (X_min) and maximum (X_max) values for your dataset's feature. For each data point (X), subtract the minimum and divide by the range (max - min). Values are mapped to the [0] range, keeping the original data's shape but changing the scale (GeeksforGeeks, 2025)