A

accuracy

Sapient Eval is an open-source, AGPL-3.0+ compliant benchmarking framework for evaluating AI models across accuracy, speed, efficiency, and reasoning performance. Built around a modular, spec-driven architecture, it enables users to define industry-specific evaluation standards, run reproducible tests on locally hosted or remote models, and compare results transparently to establish measurable, empirical performance across synthetic intelligence systems. https://roxanneardary.com/sapient-eval/

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.