Decision Tree Algorithms are classification methods used to predict possible, reliable solutions. Entropy is calculated in a Decision Tree to optimise it. These subsets that complement the Decision Tree features are chosen to achieve greater purity by calculating Entropy. It determines the purity of the component in the subgroup and splits the input accordingly. Entropy lies between 0 to 1. Gini also measures the data’s impurity to select the most appropriate split. Gini Index or Gini Impurity measures whether a division is incorrect concerning its features. Ideally, all the splits should have the same classification to achieve purity.