This document discusses using regression techniques to predict house prices. It explores using Ridge, Lasso, ElasticNet, linear regression, and gradient boosting models on a housing dataset from Ames, Iowa. Feature engineering steps are applied to the data, including filling in missing values and converting categorical variables. Models are trained and their root mean squared logarithmic error (RMSLE) scores are reported, with stacking multiple models found to improve the average score. Lasso and gradient boosting models are highlighted, and stacking them achieved the best RMSLE of 0.068. The document concludes various factors impact house prices and regularized models perform well on this dataset.