Bechtel, a global engineering and construction company, tasked the team "Never Second Quest" with building a predictive model for future construction opportunity in the United States. The team, consisting of five University of Maryland students from the school of engineering, computer science, and the Business colleges, utilized a dataset supplied by the U.S. Department of Transportation to identify key locations of critical infrastructure around the country. The model created by the team utilizes bridge and roadway structural sufficiency data to predict the year in which a bridge or road will need to be repaired. Additionally, a heat map was supplied to Bechtel of structures which will need reconstruction before 2027.

This project introduced me to a high level of Python coding. I had coding experience in MATLAB before this project, but Python was a new language for me. I used the Pandas library to preform descriptive analysis of the bridge condition data. Additionally, we used a linear regression model to predict a variable named YEARS_TILL_RECONSTRUCTION that was based on a few metrics supplied in the Department of Transportation datasheet. The bridges with less than 8 years until reconstruction were plotted on a map and presented to the Bechtel in a virtual poster presentation.