The goal of this work is to improve upon Logistic Regression’s classification accuracy for student performance by 97 percent using a Decision Tree. The sample sizes of the decision tree (10 nodes) and the logarithmic regression (10 observations) are both determined using an online sample size calculator. A student’s overall performance is determined by their combined scores in reading, writing, and arithmetic. The proposed method employs a decision tree to categorise student performance. Logistic regression is used to evaluate the results of the decision tree. Students’ academic progress is evaluated and compared based on their test scores in mathematics, reading, and writing. Logistic Regression has a lesser accuracy than Decision Tree (81%). (95 percent). A highly substantial association was found. The p-value for tree and logistic regression in two-tailed t-tests is 0.001. (p0.05). Classifying student achievement looks to be an area where the Decision Tree excels above Logistic Regression.

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