Natural Language Processing for Work Order Classification


Apply Natural Language Processing to classify work orders in order to automate the process of reviewing work order information done by Senior Engineers on two London Underground rail lines.

Summary of work undertaken

Investigated how to handle large data sets, and application of methods such as text pre-processing, lemmatization, etc. Used MLFlow to experiment and optimise logistic regression models using this data, achieving significant improvements in performance through various methods of data cleaning and manipulation.