In an automotive context, defects are always described in term of several attributes, like the vehicle involved, the component impacted, etc. However human defect diagnoses, i.e. description of the defects, could be very useful to discover further hidden information.
The aim of the work is to process, analyze and categorize the texts using built-in Cloud services and custom logics in order to label the defect descriptions. Hence the labeling will be used to perform exploratory analysis.
The analysis will be integrated into the corporate systems and will provide a deeper investigation of the defect causes.
Data Preparation, Feature Engineering, Natural Language Processing, Machine Learning, Python, Cloud Environments
Kpmg offre l'opportunità di uno stage curricolare con supporto per la compilazione della tesi.
L'inizio del progetto potrà essere fra marzo e giugno 2022.
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