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

Logistic regression

Logistic regression is a kind of supervised machine learning algorithm that is utilized to forecast a binary outcome and classify observations. Its dependent variable is a binary variable with two classes: 0 or 1. For example, it can be used to detect whether a loan applicant will default or not. It is a unique type of regression where the dependent or target variable is binary. It computes a log of the odds ratio of the target variable, which represents the probability of occurrence of an event, for example, the probability of a person suffering from diabetes.

Logistic regression is a kind of simple linear regression where the dependent or target variable is categorical. It uses the sigmoid function on the prediction result of linear regression. We can also use the logistic regression algorithm for multiple target classes. For multiple-class problems, it is called multinomial logistic regression. Multinomial logistic regression is a modification of logistic regression...