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


In this chapter, we discovered classification, its techniques, the train-test split strategy, and performance evaluation measures. This will benefit you in gaining an important skill for predictive data analysis. You have seen how to develop linear and non-linear classifiers for predictive analytics using scikit-learn. In the earlier topics of the chapter, you got an understanding of the basics of classification and machine learning algorithms, such as naive Bayes classification, decision tree classification, KNN, and SVMs. In later sections, you saw data splitting approaches and model performance evaluation measures such as accuracy score, precision score, recall score, F1-score, ROC curve, and AUC score.

The next chapter, Chapter 11, Unsupervised Learning – PCA and Clustering, will concentrate on the important topics of unsupervised machine learning techniques and dimensionality reduction techniques in Python. The chapter starts with dimension reduction and principal...