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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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) |
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| Paper Title: | Customer Churn Prediction in the Financial Sector Using Supervised Machine Learning Techniques |
| Authors Name: | SK. Tabasum Fathima , Dr. Y. Padma , Y. Yougender , S. Sreya , M. Yaswini |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192549
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| Published Paper Id: | IJNRD2304566 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Nowadays churning becomes the most popular issue in any sector because of the increase in service providers. Similarly, there are many options for customers to keep their money in whatever bank they want. This may lead to an increase in churn rate and a decrease in profit and the growth of particular banks. Identification of churn is most important to understand the reasons behind leaving the bank and can apply strategies to stop churning rate so that they can boost their business growth. This project proposes a method to predict customer churn in a Bank using machine learning techniques, a branch of artificial intelligence. The research promotes the exploration of the likelihood of churn by analyzing customer behavior. This study aims to find a machine-learning model that predicts customer churn in the taken churn_modelling dataset. The overall accuracy is taken as the metric to define the best classifier. Supervised algorithms like KNN [7], SVM [11], XGBoost [10], Logistic Regression [8], and Naïve Bayes [9]. From all algorithms, XGBoost [10] performed well with an accuracy of 86.25%, a precision of 88%, a recall of 95%, and an f1-score of 92% [1][3][4][12]. |
| Keywords: | Customer churn, Machine learning techniques [7][8][9][10][11], Data-preprocessing, Python libraries- pandas, seaborn, numpy, matplotlib, sklearn [1][6]. |
| Cite Article: | "Customer Churn Prediction in the Financial Sector Using Supervised Machine Learning Techniques", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.f564-f568, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304566.pdf |
| Downloads: | 000118755 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304566 Registration ID: 192549 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: f564-f568 Country: Vijayawada, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304566 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304566 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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