Book Image

Python Data Science Essentials - Second Edition

By : Luca Massaron, Alberto Boschetti
Book Image

Python Data Science Essentials - Second Edition

By: Luca Massaron, Alberto Boschetti

Overview of this book

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
Table of Contents (13 chapters)
Python Data Science Essentials - Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Dimensionality reduction


Oftentimes, you will have to deal with a dataset containing a large number of features, many of which may be unnecessary. This is a typical problem where some features are very inform the prediction, some are somehow related, and some are completely unrelated (that is, they only contain noise or irrelevant information). Keeping only the interesting features is a way to not only make your dataset more manageable but also have predictive algorithms work better instead of being fooled in their predictions by the noise in the data.

Hence, dimensionality reduction is the operation of eliminating some features of the input dataset and creating a restricted set of features that contains all of the information you need to predict the target variable in a more effective and reliable way. As mentioned, reducing the number of features usually also reduces the output variability and complexity of the learning process (as well as the time required).

The main hypothesis behind many...