Taming Data Imbalance with Python

datascience.aero

Table of contents

• The Imbalance Conundrum — Taming the Data Asymmetry
• Bringing Balance to the Force — Resampling to the Rescue
• Level Up with Synthetic Samples — SMOTE to the Rescue
• Hold Your Horses — Evaluation Matters!
• The Grand Finale — Ensemble to the Rescue
• Conclusion

Welcome back to another thrilling ride on the data science rollercoaster. Today, we’re diving headfirst into the wacky world of data balancing — a crucial skill in your machine learning toolkit. Picture this: you’ve got a dataset that’s as unbalanced as a seesaw with an elephant on one side and a feather on the other. Fear not! We’re about to wield our coding wands and conjure up some enchanting solutions to slay the data imbalance dragon. 🐉🔥

The implementation is available below:

Portfolio/eda/data-balancing.ipynb at main · smortezah/Portfolio

The Imbalance Conundrum — Taming the Data Asymmetry