Graduate-Admission-Predictor
Graduate Admission Predictor built using Deep Neural Network. This was my group research project during the 2nd-year as a Computer Science major.
In this experiment, we compared the performances of different kinds of algorithms to predict the graduate admission results of students at University. We evaluated each algorithm's performance using MSE and R-squared scores. The result shows that our Deep Neural Network algorithm performs better with accuracy of around 79%, compared to other algorithms such as Linear Regression (72%), Support Vector Regression (64%), Decision Tree Regression (50%), and Random Forest Regression (66%). Based on this experiment, we can conclude that Deep Neural Network algorithm produces the best accuracy in predicting the graduate admission result at university.
The dataset was taken from Kaggle, titled the “Graduate Admissions” posted by Mohan S. Acharya in April 2018.