International Journal For Multidisciplinary Research

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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Document Classification using Machine Learning

Author(s) Prajakta Pawale, Poonam Masal, Ankita Jadhav, Prof Shivraj B Kone
Country -
Abstract The main objective is to classify the sector from IT analysis papers and compare the accuracy in 2 classifiers. Classification is the sort of information analysis that may be wont to extract models describing vital information category or to predict future information trends. The foremost vital options area unit designated and information area unit ready for learning and classification. Text classification is that the method of assigning a document to at least one or additional target classes supported its contents. coaching and classification area unit performed exploitation Naïve Bayes(NB) classifiers.
Experimental results show that the ways area unit favorable in terms of their effectiveness and potency. This technique summarizes text on 10 classes like "Big Data", "Image Processing"," Information Mining", "Artificial Intelligent", "Ontology", "Data Base Management System", "Management data System" and "Software Engineering" then on. technique calculates the accuracy of testing information exploitation.
Keywords Naïve Bayes (NB), Machine Learning, Natural Language processing, TF-IDF(Term-Frequency inverse document frequency)
Field Engineering
Published In Volume 1, Issue 3, November-December 2019
Published On 2019-12-12
Cite This Document Classification using Machine Learning - Prajakta Pawale, Poonam Masal, Ankita Jadhav, Prof Shivraj B Kone - IJFMR Volume 1, Issue 3, November-December 2019.

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