Next pipeline engineers manually match anomalies from certain years to analyze the growth of the damage. The goal is to predict changes which may violate the integrity of the pipeline. The whole process is very laborious and tedious.
Using standard data science libraries in Python we were able to fully automate this task with unsupervised learning and predictive modeling. This will save hundreds of hours of work and enable pipeline engineers to spend more time on less monotonous projects.