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An In-depth Guide to PCA with NumPy

7 min readFeb 27, 2023

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Photo by Vincent Guth on Unsplash

PCA is a linear dimensionality reduction technique that transforms the p number of input variables into a smaller k (k << p) number of uncorrelated variables called principal components by taking advantage of the existing correlations between the input variables in the dataset[ref¹]

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Data Science 365
Data Science 365
Rukshan Pramoditha
Rukshan Pramoditha

Written by Rukshan Pramoditha

3,000,000+ Views | BSc in Stats (University of Colombo, Sri Lanka) | Top 50 Data Science, AI/ML Technical Writer on Medium

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