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README.md

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 [![status](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f/status.svg)](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f)
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 **ECNet** is an open source Python package for creating scalable, retrainable and deployable machine learning projects with a focus on fuel property prediction. An ECNet __project__ is considered a collection of __pools__, where each pool contains a neural network that has been selected from a group of __candidate__ neural networks. Candidates are selected to represent pools based on their ability to optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes a prediction derived from input data, and these predictions are averaged to calculate the project's final prediction. Using multiple pools allows a project to learn from a variety of learning and validation sets, which can reduce the project's prediction error. Projects can be saved and reused at a later time allowing additional training and deployable predictive models.