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Feature Scaling
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Supported formats: .csv,.xlsx,.xls,.xlsm,.xlsb,.tsv
AboutFeature Scaling
Scale numbers to 0-1 range or standardize them.Category: AI & Machine LearningPreparing income and age columns before feeding them into K-Means clusteringStandardizing features before training a regression or gradient-descent modelMaking columns with different units visually comparable on the same chart axis
The Feature Scaling tool is compatible with:.Apply Scaling before Clustering so every feature contributes equally to distance calculations, or check Stats first to see which columns have the widest spread and need it most.This tool rescales numeric columns so they share a comparable range, using either min-max normalization (mapping the column's minimum and maximum to 0 and 1) or Z-score standardization (centering values around a mean of 0 with a standard deviation of 1). Without this step, a column measured in thousands (like annual revenue) can dominate a column measured in single digits (like a 1-5 satisfaction score) in any distance- or gradient-based algorithm. It's a pure numeric transformation, no rows or categories are added or removed, only the magnitude of existing values changes.
Frequently Asked Questions:
What's the difference between the two scaling modes?
Min-max scaling maps values to a 0-1 range, while standardization centers values around a mean of 0 with unit variance, both are common prep steps for machine learning.
Why does scaling matter for ML models?
Many algorithms are sensitive to the raw magnitude of features, so putting everything on a comparable scale improves model behavior.
A standard first step for anyone preparing a CSV as input to a machine learning model rather than a report.