PHP for Data Science: Machine Learning, Data Analysis, and Visualization

Many PHP web developers believe that Data Science is the prerogative of Python. This book shatters that myth. "PHP for Data Science" is the first practical guide that shows how to effectively use PHP for collecting, processing, analyzing, and visualizing data, as well as for building simple machine learning models.

The book is intended for intermediate and advanced PHP developers who want to expand their competencies towards working with data. You don't need to learn a new programming language — all examples are written in pure PHP using popular libraries (PHP-ML, Rubix ML, PHPlot, and others).

What you will learn from the book:

• Data Collection: web scraping, working with APIs (REST, GraphQL), reading CSV, JSON, XML, and Excel files.
• Data Cleaning and Preparation: handling missing values, normalization, encoding categorical features.
• Statistical Analysis: calculating means, medians, correlations, variance, and building histograms.
• Machine Learning: linear and logistic regression, decision trees, clustering (K-means), support vector machines.
• Visualization: building charts, scatter plots, heat maps, and dashboards in PHP.
• Database Integration: working with MySQL, PostgreSQL, and MongoDB for storing and retrieving large volumes of data.
• Performance Optimization: using generators, caching, and parallel processing for working with large datasets.

Who this book is for:

• PHP developers who want to integrate analytics into their web applications.
• Automation specialists who need to process logs and metrics.
• Anyone who wants to try Data Science without leaving the PHP ecosystem.

The book is structured on the principle of "from simple to complex." At the beginning, you will learn to load and visualize data, and by the end, you will build a full-fledged data pipeline with model training and result export. All examples are accompanied by ready-to-run code that can be immediately launched on a local server or in a Docker container.

The author is a practicing PHP architect with 10 years of experience, who has implemented analytics on PHP in real projects (recommendation systems, user behavior monitoring, A/B testing). The book contains proven patterns and best practices that will save you months of experimentation.

This book will become your bridge between web development and the world of Data Science. You will be surprised at how powerful PHP can be in data analysis tasks when you know the right tools and approaches.