# Feature Scaler Scale and normalize features using various methods for machine learning preprocessing and data standardization > Canonical page: https://elysiatools.com/en/tools/feature-scaler - **Category:** Data Processing - **Keywords:** feature scaling, normalization, standardization, min-max, z-score, robust scaling, machine learning, data preprocessing, normalization ## Overview The Feature Scaler is a robust data preprocessing tool designed to normalize and standardize numerical datasets, ensuring your machine learning models perform optimally by bringing features onto a comparable scale. ## Inputs - **CSV Data Input** (textarea): Enter your CSV data here... Example: Feature1,Feature2,Feature3,Label 1.5,2.3,0.8,A 3.2,1.1,2.5,B 0.8,4.5,1.2,C 2.1,3.4,1.8,A 4.5,1.9,0.7,B - **Scaling Method** (select) - **Columns to Scale (optional)** (text): Specify which columns to scale. If empty, all numeric columns will be automatically detected and scaled. - **Scaling Parameters (for inverse transform)** (textarea): Only required for inverse transform. Paste the scaling parameters from a previous run. - **First Row Contains Headers** (checkbox): Treat first row as column headers - **Inverse Transform** (checkbox): Apply inverse transformation using provided parameters - **Output Format** (select) - **Save Scaling Parameters** (checkbox): Include scaling parameters in the output for future inverse transformations - **Output Only Scaled Data** (checkbox): Skip detailed analysis and only output the scaled data ## When to use - When your dataset contains features with vastly different ranges that could bias machine learning algorithms. - Before training distance-based models like K-Nearest Neighbors, SVMs, or K-Means clustering. - When you need to standardize data for consistent comparison or statistical analysis across different variables. ## How it works - Paste your CSV data into the input field and specify if the first row contains headers. - Select your preferred scaling method, such as Standard Scaler (Z-score) or Min-Max Scaler, from the dropdown menu. - Optionally define specific columns to scale or leave blank to auto-detect all numeric columns. - Choose your output format and process the data to receive the transformed dataset with optional scaling parameters. ## Use cases - Preparing raw sensor data for neural network training by normalizing inputs to a 0-1 range. - Standardizing financial metrics across different currencies or scales for comparative cluster analysis. - Preprocessing feature sets for gradient-based optimization algorithms to ensure faster convergence. ## Frequently asked questions ### What is the difference between Standard Scaler and Min-Max Scaler? Standard Scaler centers data around a mean of 0 with a standard deviation of 1, while Min-Max Scaler rescales data to a fixed range, typically between 0 and 1. ### Can I reverse the scaling process later? Yes, if you enable 'Save Scaling Parameters', the tool provides the necessary JSON parameters to perform an inverse transform on future data. ### What happens if I don't specify columns to scale? The tool will automatically detect and apply the selected scaling method to all numeric columns found in your CSV input. ### Which scaling method should I use for data with outliers? Robust Scaler is recommended for datasets containing significant outliers, as it uses the median and interquartile range rather than the mean and variance. ### Is my data stored on your servers? No, all data processing is performed locally in your browser to ensure your sensitive information remains private. ## Related tools - [Column Calculator](https://elysiatools.com/en/tools/column-calculator): Perform calculations on columns and add new rows - [Data Append Merger](https://elysiatools.com/en/tools/data-append-merger): Vertically append and merge multiple data tables with intelligent column matching - [Data Column Extractor](https://elysiatools.com/en/tools/data-column-extractor): Extract specific columns from tabular data with support for various formats and flexible column selection - [Row Calculator](https://elysiatools.com/en/tools/row-calculator): Perform calculations on each row and add new columns - [Column Reorder](https://elysiatools.com/en/tools/column-reorder): Reorder table columns without changing the data rows - [Coefficient of Variation Calculator](https://elysiatools.com/en/tools/coefficient-of-variation): Calculate the coefficient of variation (CV) for numerical columns to measure relative variability - [Column Remover](https://elysiatools.com/en/tools/column-remover): Remove specified columns from table data - [CSV Validator](https://elysiatools.com/en/tools/csv-validator): Validate CSV structure, detect malformed rows, unbalanced quotes and column mismatches ## Samples - [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels - [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations - [Windows String Processing - C# Samples](https://elysiatools.com/en/samples/windows-string-processing-csharp): Comprehensive C# string processing examples for Windows platform including string manipulation, splitting, joining, regex operations, and text analysis - [Apache Spark Samples](https://elysiatools.com/en/samples/apache-spark): Apache Spark big data processing framework with PySpark examples, DataFrames, SQL, and machine learning operations ## Related content - [CSV Export and Table Conversion Tools](https://elysiatools.com/en/hubs/csv-convert): Compare CSV to Excel, JSON, HTML, Markdown, XML, and text conversion tools in one hub for tabular export and interchange workflows. - [CSV Cleanup and Table Reshaping Tools](https://elysiatools.com/en/hubs/csv-utility): Compare CSV cleanup, filtering, sorting, grouping, merging, splitting, and table reshaping tools in one hub for spreadsheet and import/export workflows. - [Data Quality, Dedupe, and Anomaly Detection Tools](https://elysiatools.com/en/hubs/data-quality-anomaly-workflows): Profile CSV/JSON datasets, compare spreadsheet versions, find duplicates, outliers, missing-value issues, referential breaks, and time-series anomalies in one data-quality workflow hub.