Describe Decision tree Algorithm and what are entropy and information gain?

Decision tree is a Supervised Machine Learning approach. It uses the predetermined decisions data to prepare a model based on previous output. It follows a system to identify the pattern and predict the classes or output variable from previous output .

The Decision tree works in the following manner –

It takes the complete set of Data and try to identify a point with highest information gain and least entropy to mark it as a data node and proceed further in this manner. Entropy and Information gain are deciding factor to identify the data node in a Decision Tree.