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Position Clustering Process

Cluster students into 4 categories based on their course, moral and behavioral characteristics features and assigns a label between A to F on them.

Here I used a Variational Autoencoder to extract the latent space of course features into two dimensions in order to cluster them using K-Means Clustering algorithm.

I also used the PCA algorithm to reduce the size of course features into two dimensions for clustering.

Position Classification Process

Classify students into 4 class using a pre-trained classifier on both latent space and raw clustering of last part.

Here I trained a Neural Network classifier on clustered students in the last part to assigns the final position on each student based on their merits and efforts in their marks.

I also trained the classifier on:

• extracted latent space of VAE
• reduced dimensions of student features using PCA

So far the results of the classification on the second training seemed to be more acceptable.