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

Regression models for classification

Classification is the most utilized technique in the area of machine and statistical learning. Most machine learning problems are classification problems, such as detecting spam emails, analyzing financial risk, churn analysis, and discovering potential customers.

Classification can be of two types: binary and multi-class classification. Binary classification target variables have only two values: either 0 and 1 or yes or no. Examples of binary classification are whether a customer will buy an item or not, whether the customer will switch or churn to another brand or not, spam detection, disease prediction, and whether a loan applicant will default or not. Multi-class classification has more than two classes, for example, for categories of news articles, the classes could be sports, politics, business, and many more.

Logistic regression is one of the classification methods, although its name ends with regression. It is a commonly used binary class...