Machine Learning For Smart School Selection And Admissions

Selecting the appropriate school for their child is one of the decisions that parents are faced with in life, this process can be stressful and uncertain at times. Thus, machine learning is noted to be a possible solution that can be deployed to enhance and optimize the admissions and school selection process in the current periods. The aim of this research is to explore the framework and applicability of machine learning techniques in view of selective admissions, and smart school selection. In the early stages of our investigation, we suggest a simple to use, web based tool where with the aid of different learning machines, all the parents and guardians will be able to find the appropriate school for their child. To help the clients to select particular schools, the website collects and consolidates various information including, but not limited to the following: season activity involvement, academic performance, geographical preferences, and school ranking. This makes the process of selecting a school to attend easier and also helps parents to make right decisions for their children to attend the best school. The second section of this paper describes the procedures of implementing the machine learning techniques in detail, the overviews of the steps of feature selection, data preprocessing, and model evaluation. We also point out the challenges and ethical concerns of employing machine learning for school choice and the problem of an objective machine over a bias one.