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DataForge v1.0.0

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@MahdiKordian MahdiKordian released this 08 Oct 20:42
v1.0.0
0056638

🔥 DataForge v1.0.0

The first stable release of DataForge, a guided web app for automated data preparation for machine learning. Load a messy dataset, see what is wrong with it, apply the fixes you choose, and export model-ready data with a full report.

Open the Live Demo (it sleeps when idle, so click the wake-up button if you see a notice and wait a few seconds).

Highlights

  • Flexible loading: upload CSV, TSV, TXT, Excel, JSON or Parquet, paste from a spreadsheet, load from a URL, or type a small table. Separators, encodings and Excel sheets are handled automatically or set by hand.
  • Health dashboard: an ML readiness score and a ranked list of detected issues, so you know what to fix first.
  • Guided preparation: five steps (Clean, Transform, Encode, Scale, Split) with sensible defaults and full control over every option. Includes train/validation/test splits with stratification and oversampling or undersampling.
  • Deep analysis: nine views covering distributions, missing values, correlations, outliers, categoricals, the target, PCA and before vs after.
  • Undo, reset and audit trail: every step is snapshotted and logged, so your preparation is documented and repeatable.
  • Ready-to-share output: export the prepared data (CSV, Excel, Parquet, JSON) and a standalone HTML or Markdown report.
  • Private by design: your data lives only in the current session and is never stored.

Try it with built-in samples

Three real datasets ship with the app: Ames Housing (regression), Telco Customer Churn and Palmer Penguins (classification).

Run it locally

Requires Python 3.10+. Streamlit 1.48.1+ is installed automatically by requirements.txt.

git clone https://github.com/MahdiKordian/DataForge.git
cd DataForge
python -m venv .venv

Activate the virtual environment:

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

# Windows (Command Prompt)
.venv\Scripts\activate.bat

# macOS / Linux
source .venv/bin/activate

Then install and launch:

pip install -r requirements.txt
streamlit run app.py

License

Released under the Apache License 2.0.

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