Balanced Batch Generation for Imbalanced Datasets

# Introduction

In this project, you will learn how to implement an unbalanced data pipeline that can process imbalanced datasets and generate batches with approximately balanced class distributions. This is a common task in machine learning, where the dataset may have significantly more samples from one class compared to others, which can lead to biased model training and poor performance.

## 🎯 Tasks

In this project, you will learn:

- How to implement the functionality of upsampling and downsampling to balance the sample distribution within a batch.
- How to output a batch of samples with a sample count equal to the batch size, where the distribution of the labels within the batch is as equal as possible.
- How to test the unbalanced data pipeline to ensure it is working as expected.

## 🏆 Achievements

After completing this project, you will be able to:

- Handle imbalanced datasets in machine learning.
- Apply techniques for upsampling and downsampling to balance the class distributions.
- Implement a data pipeline that can generate balanced batches from an imbalanced dataset.