This research paper delves into the realm of business forecasting, specifically focusing on the implementation of Support Vector Regression (SVR) using the PHP-ML library. The study rigorously compares the performance of PHP-ML's SVR with Python's SVR and Linear Regression models, aiming to enhance understanding in the domain of sales prediction. Motivated by the need for accurate predictions in data-driven business environments, the research explores the practical effectiveness of PHP-ML as an alternative within the PHP ecosystem. The goal is to provide valuable insights into the viability of PHP-based machine learning solutions for improving business forecasting, addressing questions of accuracy, efficiency, and real-world applicability. The study employs historical sales data, preprocesses the dataset, and implements parameter tuned SVR models. Evaluation metrics such as Mean-Absolute-Error (MAE) and Root-Mean-Squared-Error (RMSE) are utilized for comparative analysis. The findings aim to contribute to the ongoing discourse on programming language and library selection for machine learning applications, providing practical guidance for businesses navigating predictive analytics complexities. Ultimately, the research assists decision-makers in making informed choices regarding the adoption of PHP-based machine learning solutions in sales prediction contexts.

Comparative Analysis of Sales Prediction Models