The goal of our project was to determine whether Machine Learning and predictive analytics can improve the estimated time of arrival for a shipment. Using Machine Learning Algorithms, we developed a model capable of predicting the estimated time of arrival by training the algorithms on historical shipment data, and incorporating external sources of data related to the most impactful factors regarding schedule reliability (e.g. weather and rider ratings). We found that Machine Learning in this instance might be a partial answer to this problem. In addition it was found that utilizing appropriate features as inputs to the prediction models dramatically increased the performance of the algorithms.

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