BaliNavi: Smart Travel Budgeting Platform
AI-powered smart travel platform that helps tourists plan trips to Bali through personalized destination recommendations and budget allocation based on budget, duration, group size, travel type, and destination preferences.
Overview
BaliNavi is an AI-powered smart tourism platform designed to help travelers plan trips to Bali based on their total budget, trip duration, number of travelers, travel type, and destination preferences.
The platform combines data processing, a content-based recommendation system, budget classification, and rule-based budget allocation to generate personalized destination recommendations along with estimated spending allocations.
Key Features
- Personalized Bali destination recommendations using content-based filtering.
- Budget tier classification based on the user's travel budget.
- Rule-based budget allocation for more realistic travel planning.
- Interactive trip planning interface built with Streamlit.
- REST API backend developed with FastAPI.
- Destination and budget visualizations using Plotly and Matplotlib.
- Destination photo enrichment using Google Places API.
- Destination descriptions enriched with Wikipedia data.
- Containerized backend and frontend using Docker and Docker Compose.
- Automated testing and CI workflow using GitHub Actions.
My Contributions
As part of the development team, I primarily contributed to the Data Engineering and frontend improvement areas.
My contributions included:
- Preparing and integrating the initial Bali tourism dataset.
- Performing data cleaning, exploratory data analysis (EDA), and preprocessing.
- Preparing processed and final datasets used by the recommendation system.
- Improving the data processing workflow and supporting project documentation.
- Enhancing the recommendation interface with destination photos from Google Places.
- Integrating Wikipedia-based destination descriptions with safe fallback handling.
- Improving the presentation of recommendation cards and travel information in the Streamlit frontend.
Tech Stack
Python, Pandas, NumPy, Scikit-learn, FastAPI, Streamlit, Plotly, Matplotlib, Docker, Docker Compose, Pytest, HTTPX, and GitHub Actions.