#ScikitLearn #DimentionalityReduction #PCA #SVD #MachineLearning #DataAnalytics #DataScience
Dimensionality reduction is an important step in data pre processing and data visualisation specially when we have large number of highly correlated features.
In this tutorial, we apply Principal Component Analysis and Singular Value decomposition to boston housing and MNIST handwriting dataset and observe the effects of dimensionality reduction on accuracy.
We also see how dimensionality reduction can be used to visualize data.
For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon
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