Crear12/MLPavementDistressDataAnalysis

It's a class project using Machine Learning for analysis of pavement distress data downloaded from LTPP InfoPave. In this project, construction, annual humidity, temperature and traffic data form LTPP InfoPave was adopted to predict concrete pavement cracking percentage. Linear regression model, decision tree model and random forests model were used to predict and compared with each other. Random forest has advantage in accuracy and decision tree has advantage in speed. Both models did not show their best performance due to lack of effective data.

Jupyter Notebook