Demonstrating the Benefits of Using Machine Learning to Improve Predictions with Agricultural Data

What is it about?

The article explains the typical machine learning prediction pipeline using an agricultural data example. Typical practices by machine learning practitioners are explained in the context of the choices faced by agribusiness managers.

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Why is it important?

Big data and machine learning are continuing to become increasingly widely used by businesses, policymakers and academics. To effectively use analytics tools like machine learning, practitioners need to understand the typical workflow and the important trade-offs inherent in choosing between machine learning approaches. This article helps illustrate these using an example of how machine learning can be applied to a well known agricultural data set to better predict farmers that applied for loans.