Min-Max Feature Scaler & Normalizer
Processed in your browser
Normalize numeric arrays to [0, 1] or custom [a, b] ranges for data science, machine learning models, and distance metrics.
Raw Data & Scale Target
Scaled Output Array
⚡
Direct In-Browser Handoff
Descriptive Statistics & Summary Suite
Chain this tool with complementary private browser utilities for a complete zero-server workflow:
Helpful Examples
Scale to [0, 1]
100, 250, 400, 1000 -> 0.00, 0.17, 0.33, 1.00
Range [0, 1]How to Use the Min-Max Feature Scaler & Normalizer
Data preprocessing and normalization utility. Scales arbitrary real-valued arrays into bounded numerical intervals [0, 1] using standard Min-Max transformation: x' = (x - min) / (max - min) * (b - a) + a. Preserves relative distances with zero server upload.
Privacy & Data Processing Notice
No user data leaves your browser. Computation is executed entirely on your device. We do not store, view, or transmit your inputs over our network. Your computation remains on your personal machine.
Frequently Asked Questions
Why is feature scaling essential in machine learning?
Algorithms based on gradient descent or Euclidean distance (KNN, SVM, K-Means) perform poorly when features with large ranges dominate smaller variance features.
Related Utilities
Frequently paired browser tools in Statistics & Data Analysis Tools