Principal Component Analysis (PCA) is a powerful dimensionality reduction technique that simplifies complex datasets while preserving key information. In this video, we explain how PCA works, why it’s used, and how it enhances model performance in Data Science and Machine Learning.
📊 Perfect for anyone dealing with high-dimensional data or seeking to improve visualizations and predictions!
📌 What You’ll Learn: 💡 What is PCA? – Understanding the concept behind dimensionality reduction 📈 How PCA Works – Eigenvalues, eigenvectors, and principal components made simple ⚙️ Applications – When and why PCA is essential in your ML pipeline 🧠 Benefits & Limitations – Knowing when PCA helps and when it doesn’t
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