Journal Article Builder for SkyLimit Tech Hub Academy
Journal Title
Volume and Issue
Title
Authors
Affiliations
¹ Department of Data Analytics, SkyLimit Tech Hub Academy ² Department of Computer Science, TechBridge University *Corresponding author: john.smith@skylimittechhub.com
Submission Timeline
Received: March 12, 2025 Accepted: April 7, 2025 Published: April 15, 2025
DOI
Abstract
Customer churn remains a critical challenge for retailers seeking long-term profitability. In this study, we present a machine learning model that predicts customer churn using transactional and demographic data from 30,000 retail shoppers. ...
Keywords
Introduction
Problem Statement: High customer churn rates in retail. ...
Data Description
Source: Public dataset from Kaggle. ...
Data Preprocessing
Cleaning: Imputed missing values and removed outliers. ...
Exploratory Data Analysis (EDA)
Statistical Summary: Mean age 35, standard deviation 10. ...
Modeling
Model Selection: Logistic Regression and XGBoost. ...
Evaluation
Metrics: Accuracy 85%, F1-score 0.82. ...
Interpretation
Feature Importance: Tenure and charges are key predictors. ...
Model Improvement
Improvements: Added new features and retrained model. ...
Limitations
Data Bias: Sample may not represent all demographics. ...
Conclusion
Findings: Model accurately predicts churn. ...
Recommendation
Recommendations based on the study findings. ...
References
1. Smith et al. (2023). Machine Learning for Churn. ...
How to Cite
Smith, J. A., & Desai, P. (2025). Predicting Customer Churn in Retail: A Machine Learning Model to Retain Shoppers. Data Science Journal of SkyLimit Tech Hub Academy, 2(1), 1–14. https://doi.org/10.11235/DSJ-STHA.2025.02101
Acknowledgements
This work was supported by the SkyLimit Tech Hub Retail Innovation Initiative. ...
Copyright
Copyright: © 2025 SkyLimit Tech Hub Academy. This article is the sole property of SkyLimit Tech Hub Academy, located at 10770 Columbia Pike, #300, Silver Spring, MD 20901, USA (https://skylimittechhub.org/), and is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are properly credited. All third-party content included in this article is used with permission or falls under applicable fair use policies. For permissions or reuse beyond the scope of this license, please contact the corresponding publishing team at skylimittechhub@gmail.com.
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Abstract
Keywords
Introduction
Data Description
Data Preprocessing
Exploratory Data Analysis (EDA)
Modeling
Evaluation
Interpretation
Model Improvement
Limitations
Conclusion
Recommendation
References
How to Cite
Acknowledgements
Copyright