Student

Tan Kian Aun

Supervisor

Aznul Qalid Md Sabri

Collaborator

Fylix

The growth of machine learning application usage has shown a great growth of needy to implement machine learning in every industry. However, there are lots of effort and cost to handle and maintain a variety of model for different purposes. Therefore, data scientist and AI expert has been working to automate partially or whole machine learning process. Industry like finance has been growing fast everyday. Machine learning models has to be retrained once a while to kept updated with the latest set of data. Hence, this project proposed the use of TPOT library to automated the whole machine learning pipeline. TPOT automation includes data imputation, feature selection, feature preprocessing, feature construction, model selection and parameter optimization. This project aims to help the company “Fylix” in study and evaluate how well the auto-ml library can perform in financial related dataset. From several benchmarking papers review, I found that TPOT are able to achieve better good accuracy in regression and classification compared to most of the automated machine learning library of framework available out there. Public financial related dataset has been used to evaluate the performance for regression and classification and in results it brings good evaluation scores and prediction. Experiment of TPOT training with null has been carried out to test and compare the results with dataset that is not null. Found that the performance quite good and almost similar to the training accuracy of not null dataset. The finalized model will be used for deployment purpose in a small demo system for end-user.
Keywords: automated machine learning, TPOT, financial, regression, classification