Entrepreneurship education in universities tries to produce entrepreneurs through formal education. This relatively new study program faces many challenges in creating graduates who will become a successful entrepreneur. It is expected that with a good GPA, graduates of this study program have a higher chance of becoming successful entrepreneurs. This study tries to predict the GPA from existing datasets using data mining techniques with classification methods. There are several variables involved to predict GPA, namely, attendance, gender, school origin, motivation score, capability score, and observation score. For the last three variables, the data is taken from interview when the student went through an admission selection process. There are three algorithms tested, namely Naive Bayes, Decision Tree, and Deep Learning. Using RapidMiner, the result from model validation shows that Naïve Bayes algorithm has the highest accuracy for this dataset. The model shows that although the number of female students is smaller but the number of female students whose GPA is above 3.00 is higher than male student because females are more willing to attend lectures. Despite having good capability and attitude, if the students are reluctant to attend the class then it is likely that the GPA students will be below 3.00. This study provides an overview of the use of data mining techniques to predict GPA using the Naive Bayes algorithm. In addition, it is expected that educational institutions can pay more attention to the attendance of their students in class, create teaching strategies that are able to make students present in class, especially for male students.