Synthetic Data for Machine Learning: What It Can Do
Synthetic data is useful for ML work, but only for some jobs. Here is a realistic view.
Open the Synthetic Data Generator
Teams use synthetic tabular data to share a sample without personal data, to test a pipeline before real data arrives, to build demos and to try a model setup. It does not replace real data for judging how a model will perform.
The Synthetic Data Generator takes your file and creates new rows that follow its ranges, shares and how columns move together, without copying a real row. Choose the number of rows, review the plan it shows for each column, and download the result. It works on tables, not images or text.
Check the quality. Compare column distributions between the original and the synthetic file, check that categories and ranges look right and that relationships you care about survive. Run the Data Profiler on both. A model trained on synthetic data and tested on real data tells you more than a model tested on synthetic data.
Know the limits. Rare cases and subtle relationships can be lost, and synthetic data can still reveal information if the source was tiny or unique, so be careful with small or sensitive files. Do not call it anonymous without a proper review.
Open the Synthetic Data Generator
Related tool pages
- 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 with 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.
How to use the Synthetic Data Generator: step by step
- Open the Synthetic Data Generator and load your file.
- Review the plan for each column.
- Choose the number of rows and generate.
- Profile both files and compare the distributions.
When to use the Synthetic Data Generator
- Share a sample of a dataset without real rows
- Test an ML pipeline before real data arrives
- Build a demo notebook
- Try a model setup quickly
Example
Input: real customers.csv
Output: new rows that behave like it
Frequently asked questions
What is synthetic data?
Data made by a program that follows the patterns of real data, such as ranges, shares and how columns move together, without containing the real records.
Is the synthetic data generator free?
Yes. There is no signup and no limit on how often you use it. It runs in your browser, so your file is never uploaded.
Which files can I use?
Load a CSV, TSV, Excel (.xlsx), JSON or Parquet file, then download the synthetic result in the format you choose.
How many rows can I generate?
Up to 500,000 new rows in one run. The default is the size of your file, between 100 and 50,000 rows.
Tip: Judge a model on real held-out data. Synthetic data is good for plumbing and demos.