Data cleaning and wrangling on a proprietary dataset provided by Partner Engineering, containing millions of data points on various commercial real estate properties. Transformations were made to the dataset to prepare it for data analysis, where correlations between structural attributes was explored and evaluated as potential inputs to the machine learning model.

A machine learning implementation to facilitate an autofill system by predicting the frame materials of a building based on its other structural attributes. This model utilizes a Balanced Random Forest Classifier, but also experiments with other techniques such as Logistic Regression, Decision Trees, and Synthetic Minority Over-Sampling (SMOTE).