This repository is a collection of foundational machine learning models and predictive analysis tools designed for the study of statistical learning methods. It serves as an educational resource that demonstrates the mathematical principles of classic algorithms through direct, first-principles implementation.
The project distinguishes itself by constructing models from the ground up, relying on fundamental linear algebra and calculus operations rather than high-level abstraction frameworks. Each algorithm is organized into modular, standalone scripts that mirror the sequence of mathematical derivations found in academic literature, prioritizing conceptual clarity and the exposure of internal logic over production-grade performance.
The library covers a broad range of statistical learning implementations, allowing users to prototype and execute predictive models to identify patterns within structured datasets. The source code is structured to facilitate hands-on learning, enabling the study of individual algorithms in isolation through sequential data transformation pipelines.
