Customer Churn Analysis and Prediction Using Machine Learning Techniques

S. D. Padiya, Kunal Ther, Gayatri Balode, Kalyani Mandavgade and Sayalee Uike

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 2, 415-422

Abstract: Customer retention is a big deal for making money in businesses, especially in tough fields like banking or telecom and even online shopping. I mean, if customers leave, it hurts a lot, so figuring out who might churn ahead of time is really important. This paper suggests using machine learning on old customer info to predict when people will stop using the service.The idea is to look at things like demographics and how people use the services to spot the ones most at risk. It feels like that could help narrow it down without guessing too much.Before getting into training the models, there was some work on the data. Like dealing with uneven classes where one side has way more examples, filling in missing spots, and turning categories into numbers that the algorithms can handle. That part seems necessary but kind of tedious. We tried out a few supervised models for this, stuff like Stochastic Gradient Boosting, Random Forest, the K-Nearest Neighbors one, and just plain Logistic Regression. To check how they did, metrics like accuracy, precision, recall, and that F1-score came into play. Evaluating them separately helped see differences. From the experiments, it looks like the ensemble types, you know, the boosting and forest ones, did better at predicting churn than the simpler classifiers. Not by a huge margin everywhere, but overall they stood out. I think that makes sense because they combine a lot of decisions.Businesses could use something like this to keep customers around longer, maybe by reaching out early with better offers or whatever. It might not fix everything, but proactive steps could boost loyalty a bit. The whole system shows promise for handling attrition in competitive spots.

Keywords: Customer Churn Prediction; Machine Learning; Classification; Predictive Analytics; Customer Retention; Data Preprocessing (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2613313 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2613313/IJSRST2613313 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i2:id:1466

DOI: 10.32628/IJSRST2613313

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().