Handling data imbalance using ADASYN in python | Adaptive Synthetic Sampling

In this video i have explained about handling data imbalance using ADASYN. ADASYN (Adaptive Synthetic Sampling) is an oversampling technique designed to tackle data imbalance in machine learning. It identifies regions of sparsity within the minority class and generates synthetic samples with higher density in these areas. Unlike simple replication, ADASYN creates diverse synthetic instances, effectively balancing class distribution without significantly altering the original data. This adaptive approach is particularly useful for datasets with substantial class imbalance, enhancing the performance of classifiers by providing more balanced training data. ADASYN implementation is available in libraries like imbalanced-learn in Python, offering a practical solution to handling data imbalance issues in classification tasks.

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

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

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