Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
All over the world, in one-of-a-kind sectors churn prediction performs a very vital position in the increase of the organization. For the company’s income and earnings patron churn is very harmful. The most vital step to keep away from churn is to become aware of churn and its reason, as a consequence provoke the stopping measures. Now a day’s laptop mastering performs a fundamental function to get rid of this problem. The goal of this paper is to predict the churn in banking sectors, with the aid of the use of nicely regarded laptop mastering methods like aid vector laptop (SVM) and Naive-Bayes algorithm. The classification mannequin is constructed via inspecting historic records and then making use of the prediction mannequin primarily based on the analysis. The described mannequin has a prediction fee of 94.63% for an experimental dataset containing purchaser records of an worldwide bank, gathered from Kaggle
Keywords:
Naïve-Bayes algorithm, Machine Learning (ML), Support Vector Machine (SVM)
Cite Article:
"DETERMENT OF CUSTOMER CHURN IN BANKING USING MACHINE LEARNING TECHNIQUES", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 12, page no.432 - 436, December-2022, Available :http://www.ijrti.org/papers/IJRTI2212057.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator