Generalized machine learning pipeline

A machine learning pipeline, comprising:

• an input block that receives a dataset from a data source, wherein the dataset comprises a plurality of columns that each correspond to a different feature in the dataset;
• a feature selection block that receives the dataset from the input block and reduces a size of the dataset by:
• dividing features of the dataset into a first subset of features that are correlated features and a second subset of features that are non-correlated features;
• and
• removing the second subset of features from the dataset to create a modified dataset having only columns corresponding to the first subset of features;
• a model selection block that receives the modified dataset from the feature selection block and tests performance of a plurality of models against the modified dataset by feeding one or more validation data values to each of the plurality of models and measuring a performance of each of the plurality of models, wherein the model selection block then selects a candidate model from the plurality of models based on the candidate model having a measured performance that meets or exceeds measured performances of other models in the plurality of models;
• and
• an output block that provides an output to a computational device that identifies the candidate model as being a preferred model for processing the dataset.