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DESCRIPTION:Modern paradigms of machine learning libraries often have prere
 quisites that data is provided in a numerically encoded form with all vali
 d entries. Automunge is an open source python library for preparing tabula
 r data for machine learning. Through application raw data is transformed i
 nto a form suitable for the direct application of machine learning. Subseq
 uent data\, such as may be intended to generate predictions from a trained
  model\, can then be consistently prepared on the same basis.\n\nWhat’s 
 more Automunge may serve as a platform for assembling data pipelines. A li
 brary of feature engineering transforms is available\, and through simple 
 assignment columns may be subject to feature engineering transforms\, or i
 n some cases even sets of feature engineering transforms. Missing data inf
 ill may be handled in a sophisticated manner using machine learning models
  automatically trained on the data.\n\nSpeaker(s): Nicholas Teague\, \n\nA
 genda: \nThis presentation will offer a 30-45 minutes introductory present
 ation to the Automunge library followed by a walkthrough of a demonstratio
 n Jupyter notebook\, with remaining time available for audience Q&A.\n\nOr
 lando\, Florida\, United States\, Virtual: https://events.vtools.ieee.org/
 m/240445
LOCATION:Orlando\, Florida\, United States\, Virtual: https://events.vtools
 .ieee.org/m/240445
ORGANIZER:w.macchi.us@ieee.org
SEQUENCE:6
SUMMARY:Automunge: A Tabular Data Preprocessing Platform
URL;VALUE=URI:https://events.vtools.ieee.org/m/240445
X-ALT-DESC:Description: <br /><p>Modern paradigms of machine learning libra
 ries often have prerequisites that data is provided in a numerically encod
 ed form with all valid entries. Automunge is an open source python library
  for preparing tabular data for machine learning. Through application raw 
 data is transformed into a form suitable for the direct application of mac
 hine learning. Subsequent data\, such as may be intended to generate predi
 ctions from a trained model\, can then be consistently prepared on the sam
 e basis.</p>\n<p>&nbsp\;</p>\n<p>What&rsquo\;s more Automunge may serve as
  a platform for assembling data pipelines. A library of feature engineerin
 g transforms is available\, and through simple assignment columns may be s
 ubject to feature engineering transforms\, or in some cases even sets of f
 eature engineering transforms. Missing data infill may be handled in a sop
 histicated manner using machine learning models automatically trained on t
 he data.</p><br /><br />Agenda: <br /><p>This presentation will offer a 30
 -45 minutes introductory presentation to the Automunge library followed by
  a walkthrough of a demonstration Jupyter notebook\, with remaining time a
 vailable for audience Q&amp\;A.</p>
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