How to use the Synthetic Data for Machine Learning
Load real data and get new rows that behave like it, without copying any real row.
The Synthetic Data Generator learns from your own file and makes new rows that behave like it. Number and date columns keep their range, spread and the way they move together, such as age and salary. Category columns keep their shares. Names, emails, phones and similar fields get new fake values, ids are renumbered, free text gets the same shape without the same words, and rare values are hidden. No real row is copied. It runs in your browser, and a table shows the real and the synthetic numbers side by side so you can check.
Load your real file
Check how each column will be made
Generate and compare with the real data
Tips
Choose Statistical (learns ranges and relationships) or Random sampling (only values that exist in your data).
Cities keep their real values by default. Switch a column to New fake values if you want different ones.
More real rows give better results. Under 100 rows is only a rough copy.
Use Leave out for columns you do not need, such as notes.
Review the data before sharing it publicly: synthetic data lowers risk but is not a guarantee.
It keeps ranges, shares and simple relationships, but not every pattern. Always check your model on real data.
Can I compare it with the real data?
Yes. The generator shows a comparison of the real and the synthetic columns.
How is the Synthetic Data Generator different from the Data Generator?
The Data Generator makes random rows from column types you choose, such as names, emails and dates, so the columns do not relate to each other. The Synthetic Data Generator learns from your own file and makes rows that match its ranges, shares and the way number columns move together, so the result looks like your real data. Use the Data Generator for quick fake data, and the Synthetic Data Generator when the data should behave like production.
Is synthetic data safe to share?
No real row is copied, names and emails are replaced, ids are renumbered and rare values are hidden. That lowers the risk a lot, but it is not a guarantee, so review it before you share it publicly.
Generate Test Data from Production Data: Create realistic test data from a production export without copying real rows. Names and emails are replaced, patterns are kept. Runs in your browser.
Anonymize CSV Data for Testing: Make an anonymous version of a CSV for testing: fake names and emails, new ids, same distributions. Free and in your browser.
GDPR-Safe Test Data Generator: Generate test data that contains no real people, based on the patterns of your own file. Everything runs on your device. Not legal advice; review before sharing.
Generate Synthetic Data from a CSV: Upload a CSV and generate synthetic rows that match its distributions and number relationships. Names and emails are replaced. Free, no upload.
Privacy-Safe Test Data from Real Data: Create test data that looks like production without copying production rows. Names and emails are replaced and rare values are hidden. Free, no upload.
Synthetic Dataset from an Excel File: Turn an Excel file into a synthetic dataset with the same patterns and no real records. Choose how many rows and download CSV, Excel or Parquet. Free.
Synthetic Test Data Generator: Generate synthetic test data from your own file: new rows with the same ranges, shares and relationships, and no real row copied. Free, in your browser.