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Classification approach based Customer Prediction Analysis for Loan Preferences of Customers

by Priyanka L. T, Neethu Baby
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 8
Year of Publication: 2013
Authors: Priyanka L. T, Neethu Baby
10.5120/11416-6752

Priyanka L. T, Neethu Baby . Classification approach based Customer Prediction Analysis for Loan Preferences of Customers. International Journal of Computer Applications. 67, 8 ( April 2013), 27-31. DOI=10.5120/11416-6752

@article{ 10.5120/11416-6752,
author = { Priyanka L. T, Neethu Baby },
title = { Classification approach based Customer Prediction Analysis for Loan Preferences of Customers },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 8 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number8/11416-6752/ },
doi = { 10.5120/11416-6752 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:24:08.943629+05:30
%A Priyanka L. T
%A Neethu Baby
%T Classification approach based Customer Prediction Analysis for Loan Preferences of Customers
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 8
%P 27-31
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to high competition in the business field, it is essential to consider the customer relationship management of the enterprise. Here analyze the massive volume of customer data and classify them based on the customer behaviours and prediction. The classifier will predict the customers belongs to which class that should have highest posterior probability. The valuable customer information accumulated by commercial banks, which is used to identify customers and provide decision support. The data pre-processing techniques like data cleaning and data reduction can be applied for data preparation and the dates were converted into a numerical form. A data model is generated based upon the history of the customers in the bank. Then the sample data is classified by using the Naïve Bayesian classification algorithm and placed them into the appropriate class based upon the posterior probability and based upon the posterior probability the percentage of loan sanction risk for the customers can be predicted.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CRM Data Cleaning Data Pre-processing Data Reduction Naive Bayesian Classification

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