Our client is a leading manufacturer of underground fuel tanks. Underground fuel tanks always remain under risks of possible leakage and erratic fuel dispense and delivery. Leakage implies water has seeped from the ground into the tank or fuel has leaked from the tank to the ground.

As part of the regulatory check, the fuel site owner based in Europe had to confirm that all the tanks were performing well. The existing validation process was based on statistical inventory reconciliation, and it raised too many false alarms. This validation had to be performed every week. Too many false positives led the reviewer/analyst to go through all of them and provide recommendations.

The client wanted to build a model that reduces the number of false positives in the current process (Statistical Inventory Reconciliation) of identifying a faulty tank. They also wanted to build a Shiny App to visualize the data and results.

• Irregular transmission of data from sensors
• Lack of clear definition of the failed test date
• Extremely imbalanced sample data (0.3% Target Variable)