Final Paper Builder for SkyLimit Tech Hub Academy
Final Paper Title
Subtitle
Institution
Authors
Affiliations
¹ Department of Data Analytics, SkyLimit Tech Hub Academy ² Department of Computer Science, TechBridge University
Submission Date
Abstract
Customer churn poses a significant challenge in retail, leading to revenue loss. This study investigates machine learning techniques to predict churn, focusing on XGBoost and logistic regression. Results show an 85% prediction accuracy, highlighting tenure and purchase frequency as key predictors, with implications for retention strategies.
Problem Statement
Customer churn in retail undermines profitability and brand loyalty. Existing methods lack precision in identifying at-risk customers, necessitating advanced predictive models to inform targeted interventions.
Objectives & Scope
This study aims to develop a machine learning model for churn prediction in retail, using transactional and demographic data. The scope is limited to a single dataset and assumes consistent customer behavior patterns.
Background & Literature Review
Previous research on churn prediction includes statistical models and neural networks. Smith et al. (2023) demonstrated XGBoost's superior performance, yet real-time applications remain underexplored, justifying this study’s focus.
Data Overview
The study uses a Kaggle retail dataset with 30,000 customer records and 20 features, including transaction history and demographics. Data was anonymized to address ethical concerns regarding bias and privacy.
Methodology
Data preprocessing involved imputing missing values and normalizing features. Models included XGBoost and logistic regression, evaluated using AUC, accuracy, and F1-score. Tools: Python, Scikit-learn, Pandas.
Experiments & Results
Models were trained on an 80/20 split. XGBoost achieved 85% accuracy and 0.88 AUC, outperforming logistic regression by 10%. Visualizations include feature importance plots and confusion matrices.
Discussion
Results indicate tenure and purchase frequency as primary churn predictors. The model’s assumptions about stable behavior limit generalizability, but findings support targeted retention strategies.
Conclusion
This study demonstrates the efficacy of machine learning in predicting retail churn, offering insights for academic and industry applications. Future research should explore dynamic datasets.
Future Work
Future studies could incorporate real-time data and social media metrics to enhance prediction accuracy. Collaboration with retail analytics platforms is recommended.
Recommendations
Retailers should adopt machine learning models for churn prediction and integrate them into CRM systems. Further research is needed to validate models across diverse datasets.
References
1. Smith, J., et al. (2023). Machine Learning in Retail Analytics. Journal of Data Science, 15(3), 123-134. 2. Kaggle Retail Dataset (2024). Customer Churn Data. Retrieved from Kaggle.com.
Contact Information
For inquiries, contact: Publishing team, SkyLimit Tech Hub Academy, skylimittechhub@gmail.com
Print Preview
Export as PDF
Export as TXT
Export as HTML
Final Paper
© 2025 SkyLimit Tech Hub Academy. All rights reserved.
Abstract
Problem Statement
Objectives & Scope
Background & Literature Review
Data Overview
Methodology
Experiments & Results
Discussion
Conclusion
Future Work
Recommendations
References
Contact Information