Handling Imbalanced Dataset in Machine Learning | Deep Learning Tutorial (TensorFlow 2.0 & Python)

Handling Imbalanced Dataset in Machine Learning | Deep Learning Tutorial (TensorFlow 2.0 & Python)

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. 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.

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/py/blob/master/DeepLearningML/14_imbalanced/handling_imbalanced_data.ipynb Path for csv file: https://github.com/codebasics/py/tree/master/DeepLearningML/14_imbalanced Exercise: https://github.com/codebasics/py/blob/master/DeepLearningML/14_imbalanced/handling_imbalanced_data_exercise.md

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