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

Evaluating clustering performance

Evaluating clustering performance is an essential step to assess the strength of a clustering algorithm for a given dataset. Assessing performance in an unsupervised environment is not an easy task, but in the literature, many methods are available. We can categorize these methods into two broad categories: internal and external performance evaluation. Let's learn about both of these categories in detail.

Internal performance evaluation

In internal performance evaluation, clustering is evaluated based on feature data only. This method does not use any target label information. These evaluation measures assign better scores to clustering methods that generate well-separated clusters. Here, a high score does not guarantee effective clustering results.

Internal performance evaluation helps us to compare multiple clustering algorithms but it does not mean that a better-scoring algorithm will generate better results than other algorithms. The following...