Admission Prediction

May 22, 2019
This is a simple model to explain the learning classifier system.
Let us describe each classifier type using Graduate Admission Dataset as shown in Table~\ref{tab-2}\cite{Acharya19}. Here in this text author is not going to justify the prediction of different models. This text intends to see the learning process and its step to get the output. The dataset consists of 500 rows and nine columns \textit{viz.} \textbf{Serial No., GRE Score, TOEFL Score, University Rating, SOP, LOR, CGPA, Research, and Chance of Admit}. Out of these nine columns, \textbf{Serial Number} has no role in prediction, so this column has deleted from the dataset. A column with heading \textbf{Chance of Admit} will act as response class and remaining columns act as predictors.