Metadata-Version: 2.1
Name: dedupe-fh
Version: 1.9.7
Summary: A python library for accurate and scaleable data deduplication and entity-resolution
Home-page: https://github.com/dedupeio/dedupe
Author: Forest Gregg
Author-email: fgregg@datamade.us
License: UNKNOWN
Project-URL: Documentation, https://docs.dedupe.io/en/latest/
Project-URL: Examples, https://github.com/dedupeio/dedupe-examples
Project-URL: Issues, https://github.com/dedupeio/dedupe/issues
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Dist: fastcluster (<1.1.25)
Requires-Dist: dedupe-hcluster
Requires-Dist: affinegap (>=1.3)
Requires-Dist: categorical-distance (>=1.9)
Requires-Dist: dedupe-variable-datetime
Requires-Dist: future (>=0.14)
Requires-Dist: rlr (>=2.4.3)
Requires-Dist: numpy (>=1.13)
Requires-Dist: doublemetaphone
Requires-Dist: highered (>=0.2.0)
Requires-Dist: simplecosine (>=1.2)
Requires-Dist: haversine (>=0.4.1)
Requires-Dist: BTrees (>=4.1.4)
Requires-Dist: simplejson
Requires-Dist: zope.index
Requires-Dist: Levenshtein-search

dedupe is a library that uses machine learning to perform de-duplication and entity resolution quickly on structured data. dedupe is the open source engine for `dedupe.io <https://dedupe.io>`_

**dedupe** will help you:

* **remove duplicate entries** from a spreadsheet of names and addresses
* **link a list** with customer information to another with order history, even without unique customer id's
* take a database of campaign contributions and **figure out which ones were made by the same person**, even if the names were entered slightly differently for each record

dedupe takes in human training data and comes up with the best rules for your dataset to quickly and automatically find similar records, even with very large databases.


