Comparative machine learning analysis for housing price prediction using OLS, LASSO, and Ridge regression with forecast evaluation and Diebold–Mariano testing.

This repository contains a python implementation of the Neural Networks coursework for the "Introduction to Machine Learning" Autumn module. It consists of a neural network mini-library and a neural network trained on the California House Prices Dataset to predict housing prices for a given datapoint.

Multiple linear regression model predicting median house values across California block groups using the 1990 U.S. Census dataset. Covers feature selection, coefficient interpretation, and business-relevant insights on housing price drivers. Developed as part of FIU's Machine Learning coursework.