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Convert selected categorical columns to model-friendly encoded output after explicit local processing.
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 Encode Categories succeeds.
Load a CSV file to enable processing options.
Categorical encoding converts text or label values into structured numeric columns for analysis or modeling. One-hot, ordinal, and label encodings carry different assumptions; unknown categories, missing values, high cardinality, and train-test consistency must be planned before downstream use.
Categorical Encoding Helper applies this concept to its defined inputs and workflow. It supports one-hot, drop-first, label, and frequency encoding.
After the user explicitly loads local data, the tool applies the selected workflow to local CSV file and returns encoded CSV, mapping JSON, and encoding summary. 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. Ordinal category order is not inferred automatically.
The Categorical Encoding Helper supports one-hot, drop-first, label, and frequency encoding. It warns about high-cardinality columns, limits encoded output width, and exports mapping JSON.
Selected nonnumeric columns are encoded using the chosen method. One-hot variants create indicator columns, while label and frequency modes replace selected columns with mapped numeric values.
Load a product dataset, select region and product_type, run one-hot encoding, and download the encoded CSV plus mapping JSON.
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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