Author Description

This book is about statistics, probability and machine learning in customs. But to make the learning process more effective and fruitful some aspects will be addressed through the text: Hands-on approach by employing Python language programming. No previous knowledge of the language is necessary; Extraction of features from databases based on customs day-by-day practice; Customs examples and cases that illustrate how the statistics, probability, and machine learning concepts could be applied; Use concepts like values prediction, selection of variables to build a prediction model, models to classify data, selection of variables into training and testing sets to build models and verify its efficiency, different types of errors for classification models and how all these concepts could be related with applications on customs. A brief description of concepts in each chapter of this book is: Chapter 1 explains how to use the Google Colab environment. This environment is a useful and important tool to make it easy to illustrate statistical concepts by making graphics and data counting. It also helps in solving the statistical exercises. Chapter 2 explains the course motivation, the methodology employed, and its connections with recent technological advances like Industry 4.0, the metaverse, industrial metaverse, data science, and spatial data science. It also describes the book structure and its connections with online resources like the site related to the book. Chapter 3 has a technical background that justifies why several practices will be related to employing a free programming language: Python. It also explains the preference for a specific environment to create and test codes: Jupyter notebooks on Google Colab. Finally, it introduces several practices on how to collect and treat data from the internet keeping a track of what operations had been employed by using: Literate Programming. Chapter 4 introduces the reader to the main concepts of Python programmin