Handle Imbalance Data using SMOTE ll Machine learning

Are you struggling with imbalanced datasets in machine learning? In this video, you'll learn how to handle imbalanced data using SMOTE (Synthetic Minority Over-sampling Technique) — one of the most effective techniques to balance your dataset and boost model performance!

🔍 What You’ll Learn:

What is an imbalanced dataset?

Why imbalanced data can harm your model’s performance

How SMOTE works (step-by-step explanation)

Hands-on SMOTE implementation in Python using imblearn

Logistic Regression results: Before vs After SMOTE

Model evaluation with classification report & confusion matrix

📊 Perfect for beginners and intermediate ML learners who want to improve classification accuracy on real-world imbalanced problems like fraud detection, medical diagnosis, and more.

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📁 Code Notebook: [Add your GitHub or Colab link here]
📞 Need Help? Drop your questions in the comments!

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Видео Handle Imbalance Data using SMOTE ll Machine learning канала Code to CEO