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#13 Synthetic Minority Oversampling Technique in Machine Learning | Machine Learning Tutorial | TPT

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📌 Synthetic Minority Oversampling Technique in Machine Learning | SMOTE Explained | ML Tutorial

Are you struggling with imbalanced datasets in machine learning? In this tutorial, we explain the Synthetic Minority Oversampling Technique (SMOTE) — a powerful method to handle class imbalance and improve model performance. 🔍

🎯 What You'll Learn:
What is SMOTE in Machine Learning?
Why class imbalance is a problem
How SMOTE generates synthetic samples
Practical examples of using SMOTE in Python
When to use SMOTE and when to avoid it

✅ This video is perfect for beginners and intermediate learners looking to build more accurate models for classification tasks like fraud detection, medical diagnosis, and more.

📚 Keywords Covered:
SMOTE, class imbalance, oversampling, minority class, sklearn imblearn, model accuracy, classification metrics

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