Abstract:
The tourism industry performs a major role in national and countryside
economies. Attracting more tourists to the country has continually been a
quintessential situation for governments. This research paper aims to
introduce a design a mobile web application for tourists or travelers to
reduce their difficulties while they travel. The existing applications are
reviewed first to discover the failures and the frequent features will be
merged and develop a new mobile applicationwith new features.
The problem was identified as the trip planning problem by the literature
reviews done fromthe latest existing researches and identify how the people
face problems while creating trips. This research has followed Iterative
methodology and PRINCE2 has selected as the project management
methodology.
A manual dataset was created for the development an accuracy purpose.
This mobile web application was basically to recommend trips by adding
services into this application according to the Points of Interests. The hotel
and transport services will be trained from the machine learning model and
recommended it to the user. The selected algorithm was Random Forest
classifier because it had the highest accuracy between Support Vector
Machine and K-Nearest Neighbors. Black box testing was done in design,
structure and the implementation of the functionalities. Finally, the self evaluation and evaluation by the technical experts were done by using
questionnaires
