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@ -88,3 +88,8 @@ print("Transformed Data:\n", X_pca)
#### Conclusion
Principal Component Analysis (PCA) is a powerful tool for reducing the dimensionality of datasets while preserving as much variance as possible. It is especially useful in exploratory data analysis and preprocessing for other machine learning algorithms.
#### References
- Jolliffe, I. T. (2002). *Principal Component Analysis*. Springer Series in Statistics.
- [PCA Explained](https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-118e7d7c15e8)
- [Scikit-Learn PCA Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)