PHP for Data Science: From Basics to Production

Many believe that data science is the prerogative of Python or R. But what if you are a PHP developer and want to integrate data analysis into your web projects? The book "PHP for Data Science" dispels this myth and offers a practical guide to building data analysis systems in PHP.

This book is created for web developers who are already familiar with PHP and want to expand their skills towards working with data. You don't need to be a mathematician or a data scientist — all concepts are explained from simple to complex, with real code examples.

What you will learn from the book:

• Data science basics in PHP: collecting, cleaning, and preparing data (working with CSV, JSON, databases).
• The PHP-ML library: installation and usage for classification, regression, clustering (K-means, Naive Bayes, SVM).
• Mathematical computations: working with arrays, statistical functions, linear algebra without third-party libraries.
• Data visualization: building charts and diagrams using Chart.js, pChart libraries, and SVG generation directly from PHP.
• Integration with web applications: creating APIs for predictions, dashboards, and reports on Laravel and Symfony.
• Working with big data: memory optimization, stream processing, using Redis and SPL iterators.
• Real projects: product recommendation system, anomaly detection in logs, time series forecasting.

Who this book is for:

• PHP developers who want to add data analysis to their stack.
• Team leads and architects planning to implement ML in web products.
• Students and beginners learning data science in an accessible language.
• Anyone looking for an alternative to Python for quick integration of models into the web.

The book is written by a practitioner: each section contains code that can be run immediately on a local server. You will learn not only to build models but also to deploy them to production, handle errors, and optimize performance.

"PHP for Data Science" is a bridge between web development and data analysis. After reading it, you will be able to create smart web applications that predict user behavior, analyze texts, and make automatic decisions.

Examples from the book: parsing and cleaning data from web pages, building a spam classification model, creating a simple neural network in PHP, generating reports with charts in PDF.

The book is suitable both for self-study and for use as a reference for PHP-ML and related technologies.