This project performs exploratory data analysis (EDA) on the Airbnb open dataset for New York City. It reveals insights on pricing, availability, and neighborhood trends to help understand patterns in Airbnb listings.
- Understand the distribution of listings across NYC boroughs
- Identify pricing trends by neighborhood and room type
- Detect outliers and availability patterns
- Gain insight into host behavior and top revenue areas
- Python 🐍
- Pandas 📊
- Matplotlib 📉
- Jupyter Notebook 📓
- Source: Kaggle – NYC Airbnb Open Data
- Contains details about hosts, listings, prices, availability, and locations.
- 💰 Price distributions vary significantly by borough and room type.
- 🗺️ Manhattan dominates in both listing density and average price.
- 🧹 Shared rooms and private rooms tend to be more affordable than entire homes.
- 📉 Many listings are available for fewer than 100 days/year — hinting at part-time hosts.
You may want to include plots like:
- Price vs. location scatterplots
- Availability heatmaps
- Host listing counts