Published Paper Details:

The demand for comfortable and affordable accommodations has led to an increase in the number of Pay Guest (PG) accommodations. However, finding the right PG can be a challenging task for many. In this study, we propose a Habitation Recommendation system that utilizes machine learning techniques to recommend the top 5 PGs based on the user's preferences. The system integrates Google Places autocomplete search for location-based search and a chatbot to gather user preferences based on personality traits. A survey was conducted to gather data on the behavior of residents in the PGs. The collected data were preprocessed, features were extracted, and the ML model was trained and tested. The ML model predicted the personality traits of the user, which were then used to filter the PGs based on the user's preference. Results showed that our system can effectively recommend PGs to users based on their preferences. The system can assist users in selecting the right PG based on their personality and preferences. This Habitation Recommendation system can help users in the accommodation selection process, making it more efficient and personalized.