Periodicity: Bi Annual.
Impact Factor:
SJIF:5.079 & GIF:0.416
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

Paper Template
Copyright Form
Subscription Form
web counter
web counter

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJCOA provides online manuscript tracking system.

Every issue of Journal of IJCOA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJCOA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJCOA website. For complete procedure, contact us at

SCIA Journal Metrics


Machine Learning Classification Of Active

GandhiJabakumar,ArunaDevi, Dr.M.RobinsonJoel, B.Muthazhagan

Published in:   Vol. 9 Issue 2 Date of Publication:   December 2020
Page(s):   1-4 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA. SAI :

Client turnover in the banking industry has grown according to the report. Churn can be classified into a variety of types. It s common knowledge that the cost of acquiring a new client is significantly greater than that of the expense of keeping an existing one. The objective is to find the most accurate machine learning-based churn prediction systems feasible. The entire dataset will be analysed using the supervised machine learning approach (SMLT) to gather a variety of data points including variable identification missing value treatments data validation data cleaning and data visualisation Identify the confusion matrix and categorise data from the supplied credit card dataset as well as compare and assess multiple machine learning techniques performance with an evaluation classification report from the given credit card dataset.