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
#machine-learning #deep-learning #python #tensorflow #developer
