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)
1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing

Summary

In this chapter, we discovered regression analysis algorithms. This will benefit you in gaining an important skill for predictive data analysis. You have gained an understanding of concepts such as regression analysis, multicollinearity, dummy variables, regression evaluation measures, and logistic regression. The chapter started with simple linear and multiple regressions. After simple linear and multiple regressions, our main focus was on multicollinearity, model development, and model evaluation measures. In later sections, we focused on logistic regression, characteristics, types of regression, and its implementation.

The next chapter, Chapter 10, Supervised Learning – Classification Techniques, will focus on classification, its techniques, the train-test split strategy, and performance evaluation measures. In later sections, the focus will be on data splitting, the confusion matrix, and performance evaluation measures such as accuracy, precision, recall, F1-score...