Give a real-life example where handling imbalanced data is crucial.

You clearly understand the concept of imbalanced datasets, especially in real-world applications like healthcare. I’m impressed that you used a specific percentage example (2%) to explain the problem, that shows analytical thinking. Your explanation of how a model might focus too much on the majority class was logically structured and easy to follow. That’s a strong point in academic writing.

-Teacher Wela 

The most common case where handling imbalance of dataset is health care system, especially cancer diagnosis.

>> The most common case where handling an imbalanced dataset is crucial is in the healthcare system, especially in cancer diagnosis.

For example, there are only few people who have the cancer within the entire poplulation.

>> For example, there are only a few people who have cancer within the entire population.

If we train a deep learning model with this dataset, this model might be focus on learn the feature of last 98 percent (normal case).

>> If we train a deep learning model with this dataset, the model might focus on learning the features of the remaining 98 percent (normal cases).

And this model finally can\'t detect the cancer and it loss its use.

>> As a result, the model cannot detect cancer and loses its usefulness.

So if you want to train your model more accurate, you have to use some balancing methods such as oversampling.

>> So, if you want to train your model to be more accurate, you have to use balancing methods such as oversampling.