UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 12
December-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2312452


Registration ID:
529999

Page Number

e422-e426

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Title

Customer churn prediction using ML-literature survey

Abstract

One of the biggest problems businesses confront is customer churn. For business owners, operations, and development, anticipating client attrition and attempting to keep consumers have become critical issues. The secret to a company's long-term success is churn prediction. The number of customers a business lost over a specific period of time divided by the number of customers it had at the start of that period is how churn rate is determined. In this research, we predict turnover rate using machine learning algorithms.

Key Words

Customer Churn, Machine Learning, Deep Learning

Cite This Article

"Customer churn prediction using ML-literature survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.e422-e426, December-2023, Available :http://www.jetir.org/papers/JETIR2312452.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Customer churn prediction using ML-literature survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppe422-e426, December-2023, Available at : http://www.jetir.org/papers/JETIR2312452.pdf

Publication Details

Published Paper ID: JETIR2312452
Registration ID: 529999
Published In: Volume 10 | Issue 12 | Year December-2023
DOI (Digital Object Identifier):
Page No: e422-e426
Country: PUNE, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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