This brings us to the end of the book. In this chapter, we learned all about dimensionality reduction and its various uses. We learned about PCA and applied it to the Boston dataset. We then learned about SVD and used it to compress an image. Finally, we learned about low-dimensional representation and MDS, and applied it to the iris dataset.
We have covered a lot of topics related to statistical modelling in this book, which should help you to analyze data more efficiently. You now have the ability to make sense of huge collections of data more easily than ever before. I hope this book has helped you in your quest to become a statistician or data scientist!
