Handle the Imbalanced dataset using Oversampling Techniques

[Priyanka142806]

ISSUE
As, 16% employee Attrition was there. The number of 0's(representing employee stayed) in the target variables are more than 5 times than that of 1's(representing employee Attrition) in the target variable.
Imbalanced dataset pose a challenge for predictive modeling as most of the machine learning algorithms used for classification are designed around the assumption of an equal number of examples for each class. So, training a model on imbalanced dataset results in poor predictive performance, specifically for the minority class. This is a problem because typically, the minority class is more important and therefore the problem is more sensitive to classification errors for the minority class than the majority class.

SOLUTION
The only solution to the issue is balancing the imbalanced dataset. One of the techniques to balance the dataset includes oversampling.
Oversampling involves selecting examples from the minority class with replacement and supplementing the training data with multiple copies of this instance, hence it is possible that a single instance may be selected multiple times.