Recurrent Neural Networks (RNN) are used for sequential modeling on datasets where high autocorrelation exists among observations. For example, predicting patient journeys using their historical dataset or predicting the next words in given sentences. The main commonality among these problem statements is that input length is not constant and there is a sequential dependence. Standard neural network and deep learning models are constrained by fixed size input and produce a fixed length output. For example, deep learning neural networks built on occupancy datasets have six input features and a binomial outcome.