Mastering Predictive Analytics with scikit-learn and TensorFlow

Summary

In this chapter, we talked about feature selection methods, how to distinguish between useful features, and features that are not likely to be helpful in prediction. We talked about dimensionality reduction and we learned how to perform PCA in scikit-learn. We also talked about feature engineering, and we tried to come up with new features in the datasets that we have been using so far. Finally, we tried to improve our credit card model by coming up with new features, and by working with all of the techniques that we learned in this chapter. I hope you have enjoyed this chapter. 

In the next chapter, we will learn about artificial neural networks and how the tensorflow library is used when working with neural networks and artificial ...