Paper Title
CUSTOMER CHURN PREDICTION
Article Identifiers
Authors
Kanak Ashish Rai , Viraj Chheda , Pratham Koltharkar , Sameer Shaikh
Keywords
customer churn prediction, predictive ananlysis, predictive models
Abstract
This research focuses on customer churn prediction, a crucial aspect for businesses operating in subscription-based models. Leveraging advanced analytics and machine learning, the study integrates historical customer data, behavioral patterns, and demographics to develop robust predictive models. Through preprocessing, feature engineering, and the application of algorithms like decision trees and neural networks, the models are fine-tuned to accurately forecast customer attrition. The investigation also explores key factors influencing churn and addresses ethical considerations, providing businesses with actionable insights to implement targeted retention strategies and foster long-term customer loyalty.
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How To Cite (APA)
Kanak Ashish Rai, Viraj Chheda, Pratham Koltharkar, & Sameer Shaikh (March-2024). CUSTOMER CHURN PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), b146-b149. https://ijnrd.org/papers/IJNRD2403113.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : b146-b149
Other Publication Details
Paper Reg. ID: IJNRD_214615
Published Paper Id: IJNRD2403113
Downloads: 000121980
Research Area: Information TechnologyÂ
Country: Mumbai, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403113.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403113
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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