How to handle imbalanced datasets in Python

In this video, you will be learning about how you can handle imbalanced datasets. Particularly, your class labels for your classification model is imbalanced (one class is significantly larger than the other which essentially gives rise to a majority class and minority class). Here, we will use the imbalanced-learn Python library to perform random undersampling and random oversampling so that you can address this issue of imbalanced datasets. 🌟 Download Kite for FREE https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=dataprofessor&utm_content=description-only Code: https://github.com/dataprofessor/imbalanced-data ⭕ Support my work: 🌟 Subscribe to the Coding Professor channel https://www.youtube.com/channel/UCJzlfIoF8nmWqJIv_iWQVRw?sub_confirmation=1 🌟 Subscribe to the Data Professor https://www.youtube.com/dataprofessor?sub_confirmation=1 🌟 Join the Newsletter of Data Professor http://newsletter.dataprofessor.org 🌟 Buy me a coffee https://www.buymeacoffee.com/dataprofessor ⭕ Recommended Books: 🌟https://kit.co/dataprofessor ✅ Python Basics: A Practical Introduction to Python 3 https://amzn.to/3awdWgm ✅ Learn Python Programming (The no-nonsense, beginner's guide) https://amzn.to/2RFpSpn ✅ Learn to Program with Minecraft https://amzn.to/3x2MujZ ✅ Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners https://amzn.to/2QzkyDs ⭕ Disclaimer: Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents. ⭕ Stock photos, graphics and videos used on this channel: ✅ https://1.envato.market/c/2346717/628379/4662 #python #data #datascience #dataprofessor