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The ultimate goal of this stage is to create, train, and test the ML model. The learning process is continued until it provides an appropriate degree of accuracy on the training data. A set of statistical processing processes is referred to as an algorithm. The type of algorithm used is determined by the kind (labeled or unlabeled) and quantity of data in the training data set, as well as the problem to be solved. Different ML algorithms are used concerning labeled data. The ML algorithm adjusts weights and biases to give accurate results.