Learn the most useful feature engineering techniques to convert numerical values into useful information for your predictive model using Sklearn, Numpy and Python
Feature engineering is an essential step in a machine learning pipeline, where raw data is transformed into more meaningful features that help the model better understand the relationships in the data.
Feature engineering often means applying transformations to the data at hand to overwrite or create new data that, in the context of machine learning and data science, is used to train a model that can perform better thanks to these transformations.
In this article, we will explore advanced feature engineering techniques for handling numeric values with Python’s Scikit-Learn library (which can be used via the BSD 3-Clause License for this work), Numpy, and more to make your machine learning models even more effective.
In summary, by reading this article you will learn:
- A robust list of feature engineering techniques for numerical data from the Scikit-Learn, Numpy and Scipy suites to improve the performance of machine learning models