PCA

Summary

PCA is a statistical technique used for dimensionality reduction and data exploration. It aims to identify a dataset's most essential features or patterns by transforming it into a new set of uncorrelated variables known as principal components.

Contents

1. Introduction to PCA
   • Dimensionality
2. Mathematical Overview
   • Standardization
   • Covariance Matrix
   • Eigen Vectors and Eigen Values
   • Feature Vector
   • Align Data
3. Code Overview
   • Implementation Manually
   • Implementation in Scikit Learn
4. sklearn.decomposition.PCA()
   • Parameters
   • Attributes
5. End
   • References
   • Singular Value Decomposition (SVD)

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Percentile

Summary

a statistical measure used to describe the distribution of a dataset and determine the relative position of specific data points within that distribution.

Contents

1. Introduction to Percentiles
2. Calculating Percentiles
3. numpy.percentile