Ever booked a hotel, only to realize it’s right next to a noisy highway or nightclub?
I built a smart travel assistant that helps you find quiet, clean-air hotels — before you pack your bags. 
Project: Hotel Noise & Air Quality Intelligence App
Built with: Streamlit, Pandas, OpenWeather API, Python
Dataset: 1M+ hotel listings → Sampled 10k+ for demo
GitHub Repo
Key Features:
- Noise Category Prediction from hotel descriptions, facilities, and location context
- Real-time AQI Estimation using hotel GPS and OpenWeather Air Pollution API
- Interactive UI to explore cities, filter quiet hotels, and preview booking quality
- Scalable Design (ready for Booking.com / Airbnb API integration)
Why this matters:
- 100M+ travelers book online monthly
- Noise is a top-3 reason for poor hotel reviews
- Business travelers will pay for peace and air quality
- No major travel platform currently solves this
Skills Demonstrated:
- Data wrangling from 2GB raw CSV hotel metadata
- Real-time API integration + geospatial logic
- Exploratory NLP-based noise inference
- Front-end dashboard deployment with Streamlit
This is just the proof-of-concept. A full-scale MVP could:
- Predict future noise based on local events/construction
- Personalize results by user sensitivity to noise or air
- Offer one-click “quiet” booking routes for business travelers
Looking for roles in:
- Data Science
- ML Engineering
- TravelTech Innovation
Let’s connect if you’re hiring, collaborating, or just curious.
Feedback and suggestions welcome!
#DataScience streamlit #OpenData #AIForTravel #EnvironmentalTech #NoisePollution #OpenWeather #Python #MachineLearning #AirQuality #LinkedInPortfolio #TechForGood