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Advanced Techniques in Predicting Student Dismissal Fuzzy Soft Sets vs. Machine Learning
Abstract
Many times in our daily routines, we face challenging decisions that require careful consideration and analysis due to their complexity and significance. Achieving the most effective solution often involves weighing multiple factors relevant to the situation. This study aims to apply a structured decision-making process by analogizing a community issue to a student scenario, illustrating a methodical approach to resolve complex problems using fuzzy soft sets. The same dataset has been applied to Machine learning models such as Random forest, Support vector machine and Naive Bayes. Out of which Random forest achieves the highest accuracy exactly matching the Fuzzy soft set results. Hence forth by using Fuzzy soft sets and ML based models we can predict the student dismissal rate and provide suggestions and improvements in reducing the dismissal and increasing the retention rate.
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