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Sale Forecasting for Hot-Drink Productivity Using Naïve Bayesian Classification

http://hdl.handle.net/20.500.12678/0000003406
8d3673d3-fbae-461b-a811-a4de2b38860b
34c77027-cd12-4d83-b2da-eea0159ba88b
None
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psc2010paper psc2010paper (219).pdf (232 Kb)
Publication type
Article
Upload type
Publication
Title
Title Sale Forecasting for Hot-Drink Productivity Using Naïve Bayesian Classification
Language en
Publication date 2010-12-16
Authors
Hlaing, Su Su Swe
Htun, Thaung Myint
Description
Classification is one of the most popular data mining tasks with a wide ranges of application and lots of algorithms have been proposed to build scalable classifiers. Several data mining techniques and classification methods have been widely applied to extract knowledge from databases. Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it the conditional independence assumption on which it is based, rarely true in real-world applications. This system will present sale forecasting productivity using Naïve Bayesian Classification.
Keywords
data mining, classification, forecasting, productivity
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1160
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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