Wage Modelling & Regression Analysis

Project overview

A regression coursework study combining analytical derivation, gradient descent and reproducibility checks.

A transparent regression study using wage data and a small derived overtime example. It connects analytical derivation, from-scratch gradient descent, feature scaling and statistical cross-checks. Reported improvements concern computational convergence, not predictive generalisation; the two-feature example contains only ten derived rows.

Inside this build

• Implemented through-origin, intercept-and-slope and two-feature regression models in Python.
• The LinkedIn project description reports comparing ten shared learning rates through 20 deterministic fits.
• The LinkedIn project description reports convergence reductions from 173 to two iterations and from 401 to 59 within stated error tolerances.
• Cross-checked fitted objectives against closed-form least-squares references and Minitab.
• Added reproducibility and guardrail tests with documented derivations and reporting sources.

Source code & links