Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

SVM classification

SVMs are the most preferred and favorite machine learning algorithms by many data scientists due to their accuracy with less computation power. They are employed for both regression and classification problems. They also offer a kernel trick to model non-linear relationships. SVM has a variety of use cases, such as intrusion detection, text classification, face detection, and handwriting recognition.

SVM is a discriminative model that generates optimal hyperplanes with a large margin in n-dimensional space to separate data points. The basic idea is to discover the Maximum Marginal Hyperplane (MMH) that perfectly separates data into given classes. The maximum margin means the maximum distance between data points of both classes.


We will now explore some of the terminology that goes into SVM classification:

  • Hyperplane: Hyperplane is a decision boundary used to distinguish between two classes. Hyperplane dimensionality is decided by the number of features...