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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
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| Paper Title: | Predicting customer churn using Machine learning |
| Authors Name: | Deepanker.P , Dyuthi TG , Divij AP , Bilqis Anjum |
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IJRTI_208390
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| Published Paper Id: | IJRTI2512057 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | |
| Abstract: | Customer churn represents a significant challenge for businesses across various industries, as retaining existing customers is often more cost-effective than acquiring new ones. This research addresses the critical need for proactive churn management by developing a machine learning (ML)-based predictive model to identify customers at high risk of attrition. We leverage historical customer data, including usage patterns, billing information, and demographic features, to train and validate several classification algorithms, including Logistic Regression, Random Forest, and Gradient Boosting Machines (GBM). The study focuses on feature engineering techniques to extract meaningful insights from raw data and employs rigorous evaluation metrics, such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic curve (AUC-ROC), to determine the optimal model performance. Our findings indicate that the Gradient Boosting Machine model consistently outperforms the others, achieving an AUC-ROC of [Insert Your Specific Result, e.g., 0.91]. The implementation of this robust predictive framework enables businesses to allocate resources effectively, facilitating targeted retention campaigns and personalized interventions to mitigate churn rates and maximize long-term profitability. |
| Keywords: | Customer Churn Prediction; Machine Learning; Predictive Modeling; Classification Algorithms; Customer Relationship Management (CRM); Data Mining. |
| Cite Article: | "Predicting customer churn using Machine learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a468-a471, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512057.pdf |
| Downloads: | 000179 |
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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 |
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Published Paper ID: IJRTI2512057
Registration ID:208390
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier):
Page No: a468-a471 Country: Bangalore, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512057 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512057 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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