Fig. 1

Standard flowchart for creation of a machine learning model to study biological data. The figure here shows the steps followed in order to create a machine learning model that can successfully study different types of biological data. The data is initially split up into training and testing sets. Each object of the training set is associated with a feature vector, which is passed into the required machine learning algorithm. After manipulating the various parameters of the model, a resultant machine learning model for prediction is developed. This model is then checked by passing the objects of the testing set through it. The resultant output accuracy determines the usefulness of the created model