Using SMOTE and TOMEK Link Sampling Techniques to Address Imbalanced Data Challenges in the Machine Learning models

Author(s)	Vaibhav Tummalapalli
Country	United States
Abstract	Imbalanced datasets pose significant challenges in machine learning, particularly in the automotive industry, where predicting rare events such as customer acquisition or retention is critical. Synthetic Minority Oversampling Technique (SMOTE) and TOMEK Link undersampling methods offer powerful solutions to balance these datasets and improve model performance. This paper explores these techniques in the context of customer acquisition models for a major luxury automotive brand, demonstrating how they enhance predictive accuracy and stability.
Keywords	Sampling, Synthetic Minority Oversampling, TOMEK Link undersampling, Imbalanced Classes, Propensity Modeling.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-25
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9552
Short DOI	https://doi.org/hbb8f5

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International Journal on Science and Technology