arpitagoradia/Ai-programs

This is a 0-star, 0-fork project with no recent activity, indicating it is a personal learning exercise or coursework submission. The README describes standard ML workflows (preprocessing → feature engineering → training → evaluation) applied to two well-established prediction problems (space shuttle landing and heart disease diagnosis). Both datasets and problem formulations are commodity; the approaches used are textbook applications of scikit-learn classifiers. No novel techniques, architectural insights, or domain innovations are present. The project demonstrates competence in executing standard ML pipelines but provides no unique angle, abstraction, or tool that other practitioners would adopt. There is zero ecosystem, no switching costs, and trivial replicability. Frontier labs have no incentive to build or integrate this—it solves no problem they face and introduces no new capability. The low velocity and nascent state confirm this is an educational artifact, not an active tool or framework.