Oversampling is a technique used in data analysis and machine learning to address class imbalance in datasets. When one class has significantly fewer examples than another, it can lead to biased models. Oversampling involves increasing the number of instances in the minority class, often by duplicating existing data points or generating new synthetic examples. This method helps improve the performance of algorithms by providing a more balanced representation of classes. Common techniques for oversampling include SMOTE (Synthetic Minority Over-sampling Technique) and random oversampling. By using these methods, analysts can enhance model accuracy and ensure better predictions for underrepresented classes.