The fundamental objective of this study is to evaluate and contrast the performance of two innovative classification algorithms, namely Random Forest and Decision Tree. Each of the random forest and choice tree exercises had 20 participants. The academic success of a student in the past might serve as an indication of the possible future achievements the student will have. The purpose of this research is to come up with strategies that can accurately forecast how well a kid will do academically. The use of decision trees and random forests as methods for forecasting student success are now both considered to be well-established methodologies. Each of the research groups had a total of 72 participants. The accuracy of predictions made using this dataset ranges from 88.2 percent when utilising the time-tested Decision tree to 91.7 percent when utilising the innovative Random forest classifier. 0.03 is the most significant part of the fraction. The Random Forest algorithm is better to the decision tree approach for this reason. The innovative decision tree known as the random forest performs far better than its predecessor, the tried-and-true decision tree.

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