Feature Scaling
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Supported formats: .csv,.xlsx,.xls,.xlsm,.xlsb,.tsv
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About Feature Scaling
Normalize or standardize numeric CSV columns online, free. Min-max scale to 0-1 or apply Z-score standardization before feeding data into a clustering, regression, or gradient-descent model.
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, since those models treat raw magnitude as importance.
It's a pure numeric transformation, no rows or categories are added or removed, only the magnitude of existing values changes, so the shape of each column's distribution is preserved even as its scale shifts.
Category: AI & Machine LearningCommon Use Cases
- Preparing income and age columns before feeding them into K-Means clustering
- Standardizing features before training a regression or gradient-descent model
- Making columns with different units visually comparable on the same chart axis
- Normalizing sensor or measurement data before running a distance-based similarity search
- Rescaling survey scores and revenue figures onto the same 0-1 range for a composite index
Key Features
- Min-Max Normalization (0-1)
- Z-Score Standardization
- Per-Column Selection, scale only the numeric columns that need it
- Preserves Original Row Order
- Runs entirely client-side on the full dataset, no row sampling
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.
Frequently Asked Questions
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.
Many algorithms are sensitive to the raw magnitude of features, so putting everything on a comparable scale improves model behavior, especially distance-based methods like K-Means or KNN and gradient-descent-trained models.
Min-max is a good default when you want a bounded 0-1 range, e.g. for neural networks or visual comparison. Z-score standardization is usually preferred when your data has outliers or when the algorithm assumes roughly normal-distributed input, like linear regression or PCA.
No, scaling only changes magnitude, it doesn't detect or remove anomalies. Use the Outliers tool first if you need to handle extreme values before or instead of scaling.
Yes, scaling is applied per column, so you can normalize the numeric features you need while leaving IDs, categories, or dates unchanged.
No, only the values inside the selected columns are rescaled, row order is preserved exactly.
A standard first step for anyone preparing a CSV as input to a machine learning model rather than a report.
Read the related guide →