Tourism is an important industry and a popular recreational activity done by millions around the world.
Selecting a tourist destination from the available information is one of the most difficult tasks for tourists while
planning travel, before and during their trip. Also, another important task for visitors is to plan and organize
travel routes that include many points of interest (POIs). In this proposed system, the unique preferences of
each user are taken into account and the system aims to guide the visitor in arranging the route according to
his/her areas of interest. The database is collected from tourism review websites such as TripAdvisor &
Holidify using web scraping and the YELP database is used for reference.
This approach gathers URLs of various tourist attractions from TripAdvisor and Holidify and in stages,
information about tourist attractions and reviews will be pulled from the URLs collected. K-Means clustering
and KNN methods are applied to cluster nearby attractions and hotels, Logistic Regression to perform
Sentimental Analysis on users' comments, and Transfer Learning using the VGG 16 module to locate the place
where the image provided by the user was captured, a Hybrid ML model will be constructed. The generated
model will be trained and tuned based on the features derived from the data collected. As a result, a best model
will emerge that can create numerous suggestions and itineraries for consumers.
Software And Hardware