Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Classification with support vector machines


The support vector machines (SVM) can be used for regression that is support vector regression (SVR) and support vector classification (SVC). The algorithm was invented by Vladimir Vapnik in 1993 (see http://en.wikipedia.org/wiki/Support_vector_machine). SVM maps data points to points in multidimensional space. The mapping is performed by a so-called kernel function. The kernel function can be linear or nonlinear. The classification problem is then reduced to finding a hyperplane or hyperplanes that best separate the points into classes. It can be hard to perform the separation with hyperplanes, which lead to the emergence of the concept of the soft margin. The soft margin measures the tolerance for misclassification and is governed by a constant commonly denoted with C. Another important parameter is the type of the kernel function, which can be one of the following:

  • A linear function

  • A polynomial function

  • A radial basis function

  • A sigmoid function...