Chapter 3. The Data Pipeline
So far, we've explored how to load data into Python and process it to create a bi-dimensional NumPy array containing numerical values (your dataset). Now, we are ready to immerse ourselves fully in data science, extract meaning from data, and develop potential data products. This chapter on data treatment and transformations and the next one on machine learning are the most challenging sections of the entire book.
In this chapter, you will learn how to:
- Briefly explore data and create new features
- Reduce the dimensionality of data
- Spot and treat outliers
- Decide on the best score or loss metrics for your project
- Apply scientific methodology and effectively test the performance of your machine learning hypothesis
- Reduce the ...
Get Python Data Science Essentials - Second Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
