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LDA is a supervised generalization machine learning algorithm that aims to find a linear combination of features that best separates the classes in a dataset.


Just like the Principal Component Analysis(PCA), it is a dimension reduction algorithm, These algorithms are a common choice for dimensionality reduction, in this article we are going to compare them and observe in what situation each algorithm works best. We already discussed the PCA in the prior articles of this series, Let us commence by observing what the PCA algorithm is all about as we will discuss it mostly, finally we will compare their performances on a simple dataset and in the strategy tester, make sure you stick to the end for awesome data science stuff.

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