Modelling the demand trend for automobile parts using machine learning methods

This research utilises the automotive spare parts sales dataset, containing 5,000 spare parts. Being able to accurately model demand trend for spare parts helps in reducing warehousing costs, production costs, and could result in higher sales via fulfilling sales orders in a timely manner. In order to model the demand for automobile spare parts we build machine learning models using K-nearest neighbour, support vector regressor, random forest, gradient boosting, back propagation neural networks, and deep neural networks. We conclude that deep neural networks performs better than the rest of the machine learning models on the spare parts dataset for demand trend modelling with a RMSE validation score of 6.25. After a thorough literature review, it is observed that there are few previous researches done on gradient boosting regression for demand trend modelling. Our research proves that gradient boosting's performance is better than other traditional machine learning models such as K-nearest neighbour, random forest, and support vector regressor.