Prediction-of-Failures-in-the-Air-Pressure-System-of-Scania-Trucks-

Scania dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS) which generates pressurised air that are utilized in various functions in a truck, such as braking and gear changes. The datasets’ positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. The dataset consists of data collected from heavy Scania trucks in everyday usage. These are failure cases of the trucks during operation, and our task is to predict whether a given failure is caused due to a specific component of the Air Pressure System. This may help in avoiding failure during truck operation and thereby reducing maintenance cost. Here in code_1, we are pulling 'aps_failure_training_set' and 'aps_failure_test_set' data in csv file and by cleaning the pulled data we are exporting output in 'APSTrain' and 'APSTest' csv files which can be directly used in code_2 to build the predictive model. (In code_1 as well we are building basic ML model).