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AI Data Cleaner

Intelligently clean and preprocess data, auto-handling missing values, outliers, duplicates, format inconsistencies and more - make data instantly usable

Data Cleaner

Interactive tool coming soon

Features

  • ✓ Auto-detect and handle missing values with delete, mean/median/mode fill, interpolation strategies
  • ✓ Smart outlier detection using IQR, Z-Score, Isolation Forest and other methods
  • ✓ Auto deduplication with exact and fuzzy matching (configurable similarity threshold)
  • ✓ Smart data type inference and format unification, auto-convert dates, numbers, categories
  • ✓ Generate detailed data quality reports with before/after cleaning comparison statistics

How to Use

  1. Upload data files in CSV, Excel, JSON and other formats
  2. AI auto-analyzes data quality and shows issue overview
  3. Choose cleaning strategy (automatic/manual configuration for each rule)
  4. Execute cleaning, preview results, export clean data files

FAQ

Which data file formats are supported?

Supports CSV, TSV, Excel (xlsx/xls), JSON, Parquet, SQLite database files and other mainstream formats. Single files up to 500MB, extra-large files support chunked processing.

Will data cleaning change the original data?

No. All cleaning operations are performed on copies, original data is fully preserved. Every operation is reversible, supporting rollback to any historical state.

How are missing values handled?

Multiple strategies: delete rows/columns with missing values, mean/median/mode fill, forward/backward interpolation, KNN fill, AI prediction fill. Auto-selects best strategy based on field type.

Can it handle unstructured data?

Supports partial unstructured data processing including text cleaning (remove HTML tags, special characters), address standardization, name formatting, phone number unification. Complex NLP tasks can leverage AI models.

What formats can cleaned data be exported to?

Supports CSV, Excel, JSON, Parquet, SQL INSERT statements and more. Can also push directly to databases (MySQL, PostgreSQL, MongoDB) or generate Python/R cleaning scripts.