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Reseach Article

Classical Analysis over Credit Allotment Scheme using Classification under Machine Learning Domain

by Satyam S. Sundaram, Pradeep Pant
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 32
Year of Publication: 2018
Authors: Satyam S. Sundaram, Pradeep Pant

Satyam S. Sundaram, Pradeep Pant . Classical Analysis over Credit Allotment Scheme using Classification under Machine Learning Domain. International Journal of Computer Applications. 180, 32 ( Apr 2018), 35-41. DOI=10.5120/ijca2018916864

@article{ 10.5120/ijca2018916864,
author = { Satyam S. Sundaram, Pradeep Pant },
title = { Classical Analysis over Credit Allotment Scheme using Classification under Machine Learning Domain },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 180 },
number = { 32 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2018916864 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:02:29.250346+05:30
%A Satyam S. Sundaram
%A Pradeep Pant
%T Classical Analysis over Credit Allotment Scheme using Classification under Machine Learning Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 32
%P 35-41
%D 2018
%I Foundation of Computer Science (FCS), NY, USA

As the amount of data is increasing over the tremendous rate, it is extremely viable to imply smart analysis. It deals with optimization of performance criterion dealing with examples relevant to present and past situations. Learning plays a vital role in making predictions from analysis of data set properties. Amongst the various applications in the domain of learning we have a training data set over which learning is implied as the data that is collected may contain irrelevant features that are avoidable in our process and also do not contribute towards learning .We also ensure that the selected data set suits our purpose of predicting futuristic events and unseen samples. However while dealing with problems of classification in machine learning we need to determine and draw observations relevant to a problem statement having disjoint set of training data. Mining information from data enrolls classification, clustering and other such methodologies as its subsets. The paper presents a classical descriptive procedure to compare various classification schemes under single roof and draw analysis over the best scorer in terms of accuracy to draw predictions of credit allotment to customer problem. Various data sets can be filtered by the approached schemes to make decision during binary and multi valued classification. However the paper ranks one over other to determine the best fit choice in terms of performance measures.

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Computer Science
Information Sciences


Learning Features Classification