Implemented Techniques to Handle Class Imbalance. #2

[NeonKazuha]

• Implemented sklearn libraries such as:

1. Random Undersampling.

• Technique for balancing imbalanced datasets by reducing the number of samples in the majority class. This is done by randomly removing samples from the majority class until a more balanced distribution is reached.

• In this code: Specifies to undersample majority data.

2. Random Oversampling:

• Technique to create a more balanced class distribution by increasing the number of samples in the minority class.

3. ADASYN:

• ADASYN creates synthetic samples for the minority class. However, it goes a step further by adaptively generating these samples. It considers the local density information of the minority class when creating new data points.

• Result of Handling Class Imbalances.

[NeonKazuha]

@darshbaxi Please Review.

[Samcoding5854]

Great work implementing all the Techniques used for Class imbalance handling.