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Evaluating different machine learning algorithms in academic performance prediction

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Machine-Learning-For-Academic-Performance-Prediction

Collaborators: Ricki Su, Rachel Wang

In this project, we analyzed educational data to identify important factors that affect Portugal's high school students' performance in Math and Portuguese, and propose an efficient model to predict students' future performance.

We ran, evaluated and compared the use of classification models(with the prediction being binary pass/fail): KNN, Naive-Bayes, Decision Tree, Logistic Regression, SVM, and a regression model (with the prediction being score): Linear Regression.

Presentation slides: https://docs.google.com/presentation/d/1IO1v3k88FIptTl-7-zbTiXCwrq5OLcuM1xhsTzzwrKo/edit?usp=sharing

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Evaluating different machine learning algorithms in academic performance prediction

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