Handling data imbalance using SMOTE in python | Synthetic Minority Oversampling Technique

In this video I have explained about handling data imbalance technique SMOTE with theoretical explanation as well as practical implementation . SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for addressing data imbalance in classification tasks. It works by generating synthetic samples for the minority class by interpolating between existing minority class instances. It randomly selects a minority class instance and finds its k nearest neighbors in feature space. It then randomly selects one of these neighbors and creates a synthetic instance along the line connecting the two points. This process is repeated until the desired balance between classes is achieved. SMOTE helps improve the performance of classifiers by providing more representative samples of the minority class, thereby reducing bias towards the majority class and improving overall model accuracy and generalization.


dataset link : https://www.kaggle.com/datasets/arash...

code link: https://github.com/Rhishikesh1997/dat...

what is data imbalance and how to handle it with random undersampling and oversampling video link :    • Handling data imbalance in machine le...