Handling Imbalanced Data: The SMOTE Technique

In machine learning projects the quality and structure of data play a critical role in determining model performance. One common challenge in real-world datasets is class imbalance, where one class significantly outnumbers others. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class, resulting in misleading accuracy and limited generalization. A widely adopted solution to this problem is SMOTE (Synthetic Minority Over-sampling Technique), which generates synthetic samples for the minority class to help balance the dataset. This paper explores how SMOTE works, its advantages over traditional oversampling methods and its impact on improving model performance in imbalanced classification tasks. A practical, step-by-step implementation is also presented to illustrate how SMOTE can be applied to a real-world imbalanced dataset, making this paper a useful guide for practitioners and researchers seeking to understand and use the technique effectively.