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-learning-keras-tf-tutorial/blob/master/14_imbalanced/handling_imbalanced_data.ipynb
Path for csv file: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/14_imbalanced
Exercise: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/14_imbalanced/handling_imbalanced_data_exercise.md
Focal loss article: https://medium.com/analytics-vidhya/how-focal-loss-fixes-the-class-imbalance-problem-in-object-detection-3d2e1c4da8d7#:~:text=Focal%20loss%20is%20very%20useful,is%20simple%20and%20highly%20effective.
#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
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