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

Decision tree classification

A decision tree is one of the most well-known classification techniques. It can be employed for both types of supervised learning problems (classification and regression problems). It is a flowchart-like tree structure and mimics human-level thinking, which makes it easier to understand and interpret. It also makes you see the logic behind the prediction unlike black-box algorithms such as SVMs and neural networks.

The decision tree has three basic components: the internal node, the branch, and leaf nodes. Here, each terminal node represents a feature, the link represents the decision rule or split rule, and the leaf provides the result of the prediction. The first starting or master node in the tree is the root node. It partitions the data based on features or attributes values. Here, we divide the data and again divide the remaining data recursively until all the items refer to the same class or there are no more columns left. Decision trees can be employed...