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 regression model performance

In this section, we will review the regression evaluation measures for understanding the performance level of a regression model. Model evaluation is one of the key aspects of any machine learning model building process. It helps us to assess how our model will perform when we put it into production. We will use the following metrics for model evaluation:

  • R-squared
  • MSE
  • MAE
  • RMSE


R-squared (or coefficient of determination) is a statistical model evaluation measure that assesses the goodness of a regression model. It helps data analysts to explain model performance compared to the base model. Its value lies between 0 and 1. A value near 0 represents a poor model while a value near 1 represents a perfect fit. Sometimes, R-squared results in a negative value. This means your model is worse than the average base model. We can explain R-squared using the following formula:

Let's understand all the components one by one:

  • Sum of Squares...