PREDICTING CHANCES OF ADMISSION

The world is developing rapidly and continuously looking for the best knowledge and experience among people. This motivates people all around the world to stand out in their jobs and look for higher degrees that can help them in improving their skills and knowledge. As a result, the number of students applying for Master's programs has increased substantially.

The current admission dataset was created for the prediction of admissions into the University of California, Los Angeles (UCLA). It was built to help students in shortlisting universities based on their profiles. The predicted output gives them a fair idea about their chances of getting accepted.

In this case study,

• I have learned how to build a feed-forward neural network for a classification task using Keras.

• I have seen different hyper-parameters and how they affect the network.

• I also learned about the accuracy vs. epoch curve and how it aids in understanding how the model learns weights.

• I was able to get the test accuracy of 95% using the final model.

• In the future I can further analyze the misclassified points and see if there is a pattern or if they were outliers that our model could not identify.

• I was playing around with the other hyper-parameters to see how it affected my model.