Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1267
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFuentes, Alvaro-
dc.date.accessioned2026-07-22T05:58:15Z-
dc.date.available2026-07-22T05:58:15Z-
dc.date.issued2018-12-
dc.identifier.isbn978-1-78913-871-9-
dc.identifier.urihttp://hdl.handle.net/123456789/1267-
dc.description.abstractPredictive analytics is one of the most important technologies of our time. Every day, companies in all industries and all types of institutions are using predictive techniques to solve a wide range of problems. Although many of the main ideas and techniques have been around for many decades, the use of predictive analytics has exploded recently due to the increased ability to capture and store data, which is the raw material from which we build predictive models. There are two other big factors that explain the increasing adoption of this technology: the first is the astonishing increase in computing power, and the second is the availability of many open source software projects that have given access to professionals outside academia to many of the most powerful predictive analytics techniques. The Python programming language and its ecosystem of analytical libraries, also known as Python's data science stack, is such a project and has democratized the use of advanced analytical techniques.en_US
dc.language.isoenen_US
dc.publisherPackt Publishingen_US
dc.relation.ispartofseries1261218;-
dc.subjectHands-On Predictive Analytics with Pythonen_US
dc.titleHands-On Predictive Analytics with Pythonen_US
dc.title.alternativeMaster the complete predictive analytics process, from problem definition to model deploymenten_US
dc.typeBooken_US
Appears in Collections:E-Books

Files in This Item:
File Description SizeFormat 
Hands-On Predictive Analytics with Python.pdf6.66 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.