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Scale numeric CSV columns locally and download a normalized CSV after reviewing before-and-after ranges.
Your selected file is processed locally in your browser. ScholarTool does not upload the file to a third-party calculation or data-analysis API.
Selecting a file only records file details. Click Load CSV to parse and preview input.
Drag and drop is optional; the normal file chooser is the primary accessible control.
Results, visuals, downloads, and copy actions remain hidden until Normalize Data succeeds.
Load a CSV file to enable processing options.
Data normalization rescales numeric variables so columns with different units or magnitudes can be compared or used by scale-sensitive methods. Min-max scaling maps a range, z-score standardization centres by mean and standard deviation, and robust scaling uses median and spread measures.
Data Normalization Tool applies this concept to its defined inputs and workflow. It transforms selected numeric columns only after explicit processing.
After the user explicitly loads local data, the tool applies the selected workflow to local CSV file and returns normalized CSV, before-after range table, and range visual. Input structure, labels, missing values, and selected options must match the intended workflow. The output should be compared with the source file and the assumptions of the intended analysis before any transformation is accepted. This tool transforms a loaded file; it does not save reusable model preprocessing parameters.
The Data Normalization Tool transforms selected numeric columns only after explicit processing. It supports custom min-max ranges, Z-score standardization, robust median/IQR scaling, and max-absolute scaling.
Numeric values are parsed conservatively. Selected columns are transformed using the chosen method while missing and nonnumeric cells remain unchanged.
Load a feature table, select numeric measurement columns, run Z-score standardization, and download a scaled CSV for downstream modeling experiments.
This tool is intended for educational, estimation, and preliminary data-preparation use. Always verify critical data-processing decisions with validated workflows and qualified professional judgment before using results in real research, compliance, or engineering decisions.
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