Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Chapter 4. Machine Learning

After having illustrated all the data preparation steps in a data science project, we have finally arrived at the learning phase where algorithms are applied. In order to introduce you to the most effective machine learning tools that are readily available in Scikit-learn, we have prepared a brief introduction for all the major families of algorithms, complete with examples and tips on the hyper-parameters that guarantee the best possible results.

In this chapter, we will present the following topics:

  • Linear and logistic regression

  • Naive Bayes

  • The k-Nearest Neighbors (kNN)

  • Support Vector Machines (SVM)

  • Ensembles such as Random Forests and Gradient Tree Boosting

  • Stochastic gradient-based classification and regression for big data

  • Unsupervised clustering with K-means and DBSCAN