Credit card fraud detection, cancer prediction, customer churn prediction are some of the examples where you might get an imbalanced dataset. Training a model on imbalanced dataset requires making certain adjustments otherwise the model will not perform as per your expectations. In this video I am discussing various techniques to handle imbalanced dataset in machine learning. I also have a python code that demonstrates these different techniques. In the end there is an exercise for you to solve along with a solution link. Code: https://github.com/codebasics/deep-le... Path for csv file: https://github.com/codebasics/deep-le... Exercise: https://github.com/codebasics/deep-le... Focal loss article: https://medium.com/analytics-vidhya/h.... #imbalanceddataset #imbalanceddatasetinmachinelearning #smotetechnique #deeplearning #imbalanceddatamachinelearning Topics 00:00 Overview 00:01 Handle imbalance using under sampling 02:05 Oversampling (blind copy) 02:35 Oversampling (SMOTE) 03:00 Ensemble 03:39 Focal loss 04:47 Python coding starts 07:56 Code - undersamping 14:31 Code - oversampling (blind copy) 19:47 Code - oversampling (SMOTE) 24:26 Code - Ensemble 35:48 Exercise