The practice of learning is simply learning from involvement or observations from previous work, such as examples, or instruction, to look for patterns in data and with the help of examples, provided the system can make better decisions. The basic aim of ML is to make computers learn automatically with no human intervention and to adjust perform actions accordingly. Past data are used to train the model, and then this trained model is used to test new data and then for prediction. This is usually referred as the validation process. In this process, the ML model is evaluated for its performance measure, such as accuracy. Accuracy describes the ML model’s performance over unseen data in terms of the ratio of the number of correctly predicted features and total available features to be predicted.
