Data mining techniques are very useful in the discovery of the hidden knowledge amid a huge amount of data, as will for merging similar data objects. Different algorithms and techniques are available for data mining like clustering, classification, association rule mining and neural networks to solve problems of data discovery and arrangement. The discussed algorithms are supervised learning whose labels are defined, in which classification is the most well-known method. Classification is a widely used method for a number of useful applications like artificial intelligence, credit card rating and fraud detection, etc. A number of Weka classifiers families are available such as Bayes, Lazy, functions, Meta, misc, rules and tree with their own pros and cons. Amid these algorithms, decision trees are the simplest and easiest algorithms for understanding, decision making and to compare with others due to hierarchal structure in nature. There is a number of Decision Tree algorithms used and employed by researchers that are available in the literature. However, this study focuses on the comparison of six decision tree algorithms that are CART, J-48graft, J48, ID3, Decision Stump and Random Forest. The objective of this study is to compare various decision tree algorithms to conclude the best algorithm for a particular problem using Python and Weka tool.