Student Placement Chance Prediction Model using Machine Learning Techniques

Manike, Manoj; Singh, Priyanshu; Madala, Purna Sai; Varghese, Steve Abraham; Saleti, Sumalatha

doi:10.1109/cict53865.2020.9672372

Cited by 2 publications

(1 citation statement)

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“…This research is beneficial for enhancing the students' enrollment and finding the below targets schools in time so that necessary action can be taken to improve the performance of schools. Prediction and classification techniques were used to analyze students' performance and dropout [34].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach

et al. 2023

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Out-of-school children (OSC) surveys are conducted annually throughout Pakistan, and the results show that the literacy rate is increasing gradually, but not at the desired speed. Enrollment campaigns and targets system of enrollment given to the schools required a valuable model to analyze the enrollment criteria better. In existing studies, the research community mainly focused on performance evaluation, dropout ratio, and results, rather than student enrollment. There is a great need to develop a model for analyzing student enrollment in schools. In this proposed work, five years of enrollment data from 100 schools in the province of Punjab (Pakistan) have been taken. The significant features have been extracted from data and analyzed through machine learning algorithms (Multiple Linear Regression, Random Forest, and Decision Tree). These algorithms contribute to the future prediction of school enrollment and classify the school’s target level. Based on these results, a brief analysis of future registrations and target levels has been carried out. Furthermore, the proposed model also facilitates determining the solution of fewer enrollments in school and improving the literacy rate.

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Section: Related Workmentioning

confidence: 99%