Predicting Housing Prices in Ames, IA

This was my first standalone project in General Assembly's Data Science Immersive. Based on the classic Ames, IA housing dataset we were tasked with building a Multiple Linear Regression model to predict housing prices. The dataset contains 79 unique housing features, from square footage to the type of exterior facade on a building and everything in between. I performed a strong exploratory data analysis and utilized foundational techniques such as train-test-splitting and cross-validation. In addition, I successfully implemented lasso and ridge regularization techniques to reduce the impact of unimportant features; helping the model to identify the "signal through the noise".