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Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction

Megha Gupta1 , Anisha Patil2 , Ansh Tyagi3 , Deepanshi Singhal4

  1. Dept. of Computer Science & Engineering, ADGITM New Delhi, India.
  2. Dept. of Computer Science & Engineering, ADGITM New Delhi, India.
  3. Dept. of Computer Science & Engineering, ADGITM New Delhi, India.
  4. Dept. of Computer Science & Engineering, ADGITM New Delhi, India.

Section:Research Paper, Product Type: Journal Paper
Volume-11 , Issue-12 , Page no. 16-20, Dec-2023

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v11i12.1620

Online published on Dec 31, 2023

Copyright © Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal, “Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.12, pp.16-20, 2023.

MLA Style Citation: Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal "Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction." International Journal of Computer Sciences and Engineering 11.12 (2023): 16-20.

APA Style Citation: Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal, (2023). Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction. International Journal of Computer Sciences and Engineering, 11(12), 16-20.

BibTex Style Citation:
@article{Gupta_2023,
author = {Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal},
title = {Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2023},
volume = {11},
Issue = {12},
month = {12},
year = {2023},
issn = {2347-2693},
pages = {16-20},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5642},
doi = {https://doi.org/10.26438/ijcse/v11i12.1620}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v11i12.1620}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5642
TI - Broadening the Scope: Exploring Best Machine Learning Algorithms for Customer Churn Prediction
T2 - International Journal of Computer Sciences and Engineering
AU - Megha Gupta, Anisha Patil, Ansh Tyagi, Deepanshi Singhal
PY - 2023
DA - 2023/12/31
PB - IJCSE, Indore, INDIA
SP - 16-20
IS - 12
VL - 11
SN - 2347-2693
ER -

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Abstract

As businesses strive to maintain a competitive edge in today`s dynamic market, understanding and mitigating customer churn has become a critical imperative. This study explores the application of machine learning algorithms in Python for predicting customer churn, providing valuable insights to empower businesses in customer retention strategies. Leveraging a comprehensive dataset encompassing customer behavior, transaction history, and demographic information. Our methodology incorporates a diverse set of machine learning techniques, encompassing K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Logistic Regression, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier, and Voting Classifier. The outcomes reveal that the machine learning models demonstrate auspicious predictive capabilities, presenting businesses with a proactive means of identifying and mitigating potential churn risks. The discoveries from this investigation contribute valuable insights to the expanding realm of knowledge in customer relationship management, offering actionable guidance for businesses seeking to enhance customer retention strategies through the implementation of machine learning techniques in Python.

Key-Words / Index Term

Machine Learning Algorithm, Analysis, Best Algorithms, Customer Churn Prediction.

References

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