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Feature importance refers to a measure of how important each feature/variable is in a dataset to the target variable or the model performance. It can be used to understand the relationships between variables and can also be used for feature selection to optimize the performance of machine learning models.
End-to-end project to analyze and model concrete compressive strength data then productionize the best model to help civil engineers determine concrete structural integrity
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".