Understanding Customer Problems through Text Categorisation
Abstract: Customer problem is a common problem that needs to be handled in the company that provides support to their customer. Abundant data that it produced makes it inefficient to do it manually, which makes machine learning as an approach that could help to solve it. This project achieved a suitable approach of classifying a customer problem using text categorisation. This particular dataset is solvable when using Term Frequency-Inverse Document Frequency and one-hot encoding to generate the feature and use Logistic Regression as the classifier. Three measurement metrics, named F1 weighted score, Geometric Mean, and Indexed Balance Accuracy, was used to measure this imbalanced dataset.
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