Research Guide for ML based Smart Tourist System

Sri Lanka needs to adapt to new trends in order to give tourists better service if it hopes to dominate the world of tourism. One of the sectors producing the most money throughout the world is the tourist business. The greatest objective of the project is to assist the development of the tourism sector by recommending the suitable spots for users based on factors such as age group, sex, religious doctrine, region, travelled month, type(s) of travel group, and food choices, as ll as by suggesting the most appropriate transport systems and modes, customizing and managing travel times. Additionally, the proposed approach gives hotels and other relevant service providers the chance to tailor their operations to the demands of tourists. Also got a previous visitor data set from the Sri Lankan Tourism Board as ll as data from travelers utilizing a Google poll conducted by a travel operator. In this work, provided a categorization of AI models to foretell the best sites and the most effective transport methods. Random Forest was chosen as the classifier for this investigation because it has a 90% accurateness for certainly intended and an 86% accuracy level for transit type prediction. The other is a simple exponential time series model, which has demonstrated accuracy at a level of 84.74%. An algorithm that produces journey plans according to user choices is used to create an optimistic travel plan.