ML & Data Prep Tools

Data Normalizer opens this category because feature scaling is the step machine learning tutorials mention once and then quietly assume is done. Feed raw house prices ($85k to $420k) alongside a binary flag (0 or 1) into a k-nearest-neighbour model without normalising and the price column drowns the flag completely. Not because price matters more, but because its numeric range is five orders of magnitude wider. Min-Max Scaling maps every value into a bounded range you choose (default [0, 1]), which is what neural network activation functions and image pipelines expect. Z-Score Standardisation re-centres to zero mean and unit variance, which is what linear regression, PCA and any algorithm that assumes a Gaussian prior actually needs. The tool handles both in a single paste: comma, space, semicolon, pipe, tab or newline delimiters all work, negative values are supported without special handling, and a single outlier in the sensor-reading sample (10, 12, 11, 13, 10, 99, 11, 12) visibly compresses every other value below 0.04 under Min-Max. That is the exact behaviour that motivates reaching for Z-Score or outlier removal instead. Bar Chart Generator from the Charts and Diagrams category also appears here because normalised datasets are the ones people most often want to visualise directly after scaling.