Machine Learning for Classification of Temperature Controlled Containers Using Heavily Imbalanced Data

Sammanfattning: Temperature controllable containers are used frequently in order to transport pharmaceutical cargo all around the world. One of the leading manufacturing companies of these containers has a method for detecting containers with a faulty cooling system before making a shipment. However, the problem with this method is that the model tends to miss-classify containers. Hence, this thesis aims to investigate if machine learning usage would make classification of containers more accurate. Nonetheless, there is a problem, the data set is extremely imbalanced. If machine learning can be used to improve container manufacturing companies fault detection systems, it would imply less damaged and delayed pharmaceutical cargo which could be vital. Various combinations of machine learning classifiers and techniques for handling the imbalance were tested in order to find the most optimal one. The Random Forest classifier when using oversampling was the best performing combination which performed about equally as good as the company’s current method, with a recall score of 92% and a precision score of 34%. Earlier there were no known papers on machine learning for classification of temperature controllable containers. However, now other manufacturing companies could favourably use the concepts and methods presented in this thesis in order to enhance the effectiveness of their fault detection systems and consequently improve the overall shipping efficiency of pharmaceutical cargo.