Chapter 12
Stretching Python’s Capabilities
IN THIS CHAPTER
Understanding how Scikit-learn works with classes
Using Scikit-learn’s transformative functions
Testing performance and memory consumption
Saving time using multicore computations
If you’ve gone through the previous chapters, by this point you’ve dealt with all the basic data loading and manipulation methods offered by Python. Now it’s time to begin utilizing some more advanced instruments for data transformation and pipelining in machine learning. The final step of most data science projects is to build a data tool able to automatically transform, predict, and recommend directly from your data.
Before taking that final step, you still have to process your data by enforcing transformations that are even more radical. That’s the data wrangling or data munging part, where sophisticated transformations are followed by visual and statistical explorations, and then, eventually, by further transformations, if your explorations have pointed out something interesting to pursue.
From here onward, you use the Scikit-learn package ...
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