Due to the time-series nature of the data, cumulative and change values between measures were calculated. Cumulative values were calculated by successive addition of each time instance of a feature. Change values were calculated by measuring the difference between the previous and current time instance of a feature. Power transformations were applied to the features and to the cumulative features to increase the linearity of their relationship with product formation. Models were made using linear regression, cubic polynomial regression, random forest, gradient boosting regression, and long short-term memory (LSTM) neural networks. The linear regression, random forest, and gradient boosting regression models were all implemented in Python using conventional sci-kit learn libraries. The cubic polynomial regression model was constructed using a custom function in Python. The decision tree models each used 1000 trees and the same random state was used. The LSTM neural networks were made using the Python package Keras. Two LSTMs were constructed. The first LSTM predicts the current product. A second LSTM was constructed to predict future product formation. Both LSTMs used a sliding window of data to make predictions.