Backend | NodeJS | MongoDB |
Frontend | Android Studio - Java | |
UI-UX Design | Adobe XD | |
ML | Python in Jupyter Notebooks | Python Libraries |
Scraping was done as well
We would like to thank Maithrey Talware for his repository where he worked with a dataset for Toronto, Canada for the recommmendation system POC which can be found at https://github.com/maitreytalware/Recommender-system-for-tourists
In our Personalized Iternary Tourist Companion, TRAVERSE, we have come up with personalized choices of a tourist based on what he/she/they wants to visit in a place like monuments, museums etc and these choices are the key attributes of the created dataset, based on whose features overall ratings are given.
We were forced to create a dummy dataset due to lack of data available.
- Data extractrion, imputation and vizualizations
One hot encoding of required features
content based filtering
collaboration based filtering
display of tourist spots based on likeliness
We have used knn algorithm for model training
We've used several NPM packages like express, dotenve, nodemone etc to start and maintain our server.
We've used volley for fetching API's and Google Material Design API's.