DataForgeML
DataForgeML is an automated, end-to-end pipeline for turning raw tabular datasets into ML-ready data — without hand-writing schema logic, imputation rules, or splitting code for every new dataset.
The library is built as a one-stop solution: the goal is that a user should not need to manually assemble fragmented libraries steps or write custom glue code around it. Every phase — profiling, imputation, splitting — is designed to compose directly, driven by configuration objects rather than bespoke per-dataset scripts.
Supported input formats: CSV, TSV, Parquet, JSON, NDJSON, JSONL, XLSX, XLS, Arrow, and Feather, with automatic encoding and delimiter detection.
Documentation
Full documentation — including the complete API reference, configuration guide, and the domain concepts behind profiling, imputation, and splitting — is available at:
https://devunderdog.github.io/DataForgeML/
Installation
Install from PyPI
pip install dataforge-ml
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for dataforge-ml 2.1.0
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| dataforge_ml-2.1.0.tar.gz | 124.4 kB | Details |
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| dataforge_ml-2.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 272.4 kB
Release files / dataforge_ml-2.1.0.tar.gz
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