Smart Hostel Allocation System Using Naïve Bayes Algorithm

Abstract

This research explores the potential of automated personality classification using supervised machine learning techniques, with a specific focus on the Myers-Briggs Type Indicator (MBTI) for hostel allocation. The problem is the manual and resource-intensive allocation of hostel accommodations in educational institutions, leading to personality mismatches among roommates, which negatively impacts students' academic engagement and well-being. The aim of this research is to improve hostel room allocation by predicting student personality traits, thus minimizing personality mismatches among roommates. A Naïve Bayes classifier was applied to the MBTI personality dataset acquired from Kaggle. Various preprocessing techniques, such as tokenization, stop-word removal, and word stemming, were used to prepare the dataset for feature selection. CountVectorizer and TF-IDF methods were employed to convert text into numerical vectors for machine learning analysis. Additionally, SMOTE was implemented to address class imbalance within the dataset, ensuring better model performance. The Naïve Bayes classifier showed promising results, with scores achieved across all personality traits as 73% precision, 70% accuracy, F1 Score 70% and Recall 70% in predicting MBTI personality traits from input text. Visualizations and performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate the model's efficiency. The research findings suggest that supervised machine learning techniques can effectively predict personality traits from social media data, aiding in improved hostel room allocation and potentially benefiting broader applications in education and computational psychology. This system is recommended for educational institutions and residential complexes seeking efficient roommate pairing.