<ul><li>K-Means Clustering and PCA are powerful data science techniques used for dimensionality reduction and data exploration.</li><li>K-Means Clustering groups data points into clusters based on similarity, while PCA reduces features for easier analysis and visualization.</li><li>K-Means Clustering helps identify patterns in data, while PCA reveals variance and improves machine learning algorithms.</li><li>Both techniques have limitations, such as assuming certain data structures or relationships.</li></ul>

Understanding K-Means Clustering and PCA: Unraveling the Power of Data Science Techniques

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