Book Image

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials - Third Edition

By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the 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 (11 chapters)

Machine learning with Spark

At this point in the chapter, we arrived at the main task of your job: creating a model to predict one or multiple attributes being missing in the dataset. For this task, we can use some machine learning modeling, and Spark can give us a big hand in this context.

MLlib is the Spark machine learning library; although it is built in Scala and Java, its functions are also available in Python. It contains classification, regression, recommendation algorithms, some routines for dimensionality reduction and feature selection, and it has lots of functionalities for text processing. All of them are able to cope with huge datasets, and use the power of all the nodes in the cluster to achieve their goal.

As of now, it's composed of two main packages: MLlib, which operates on RDDs, and ML, which operates on DataFrames. As the latter performs well and is the...