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 the classification model performance

Up to now, we have learned how to create classification models. Creating a machine learning classification model is not enough; as a business or data analyst, you also want to assess its performance so that you can deploy it in live projects.

scikit-learn offers various metrics, such as a confusion matrix, accuracy, precision, recall, and F1-score, to evaluate the performance of a model.

Confusion matrix

A confusion matrix is an approach that gives a brief statement of prediction results on a binary and multi-class classification problem. Let's assume we have to find out whether a person has diabetes or not. The concept behind the confusion matrix is to find the number of right and mistaken forecasts, which are further summarized and separated into each class. It clarifies all the confusion related to the performance of our classification model. This 2x2 matrix not only shows the error being made by our classifier but also represents...