This project entails the use of Artificial Neural Network, which is a concept of Artificial Intelligence and specifically Machine Learning to produce a model from dehydration experimental data variables (Temperature, Size, Drying Time) needed to predict the moisture ratio of yam material which is beneficial for further processing into flour, flakes, etc.
This is a supervised learning task as the variables listed above are the ‘features’ while the moisture ratio the ‘label’ was available for training the model initially.

This project also entails using Tree-Based Models to quickly develop a model using an automated machine learning library - pyCaret to predict the moisture ratio of a substance

The performances of the models selected (MLFNN, Extra Trees Regressor and LSTM) were compared and results drawn.