Handling Imbalanced Dataset

This project shows how we can handle imbalanced dataset using various methods!

                              

😇 Motivation

While learning Machine Leanring, I came across few datasets which were highly imbalanced which resulted in me getting stuck in the very beginning. So I thought of making a notebook which will help in quickly refering and revising different ways to handle imbalanced datasets.

⭐ Features

1. Under-sampling
2. Over-sampling
3. imbalanced-learn module
4. Random Over-sampling and under-sampling
5. Tomek links
6. SMOTE
7. Over-sampling followed by under-sampling

• Using Recall to measure accuracy
• Performed Logistic Regression for all the preprocessed data
• Used Recall Score as metric to measure how well the model is performing!

📁 Dataset

The dataset used can be downloaded here (Kaggle) - Click to Download

❤️ Owner

Made with ❤️  by Sahil Chachra

👀 License

MIT © Sahil Chachra