Data imbalance occurs when the distribution of classes in a dataset is significantly skewed, with one class having significantly more instances than the others. To address this issue, random undersampling involves randomly removing instances from the majority class, while random oversampling involves randomly duplicating instances from the minority class. Both techniques aim to balance class distribution and improve model performance. Care must be taken to ensure that the undersampling or oversampling process does not lead to loss of important information or overfitting. Cross-validation techniques can help evaluate the effectiveness of these methods in improving model performance. Balancing data distribution is crucial for building robust machine learning models, especially in scenarios where minority classes are of interest but underrepresented in the dataset. So in this video will understand how to handle data imbalance

github link : https://github.com/Rhishikesh1997/data-imbalance

dataset link : https://www.kaggle.com/datasets/arashnic/imbalanced-data-practice