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Here I've demonstrated how and why should we use PCA, KernelPCA, LDA and t-SNE for dimensionality reduction when we work with higher dimensional datasets.

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LDA
 
 
PCA
PCA
 
 
algorithms_numpy
algorithms_numpy
 
 
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t-SNE
 
 
README.md
README.md
 
 

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Dimensionality reduction using Python

In this repository you will find 3 different use cases of dimensionality reduction algorithms in practice.

Note: In the folder algorithms_numpy you will find custom implementation of PCA algorithm using only numpy.

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Each project has its own README where you will find more information about a project itself.

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Each project has MIT license

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Here I've demonstrated how and why should we use PCA, KernelPCA, LDA and t-SNE for dimensionality reduction when we work with higher dimensional datasets.

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machine-learning pca dimensionality-reduction lda tsne kernel-pca

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