Since 2008, guests and hosts have used AirBNB to expand on travelling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in San Diego, California for 2019.
This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.
- Python
- Jupyter Notebook
- Pandas
- Matplotlib
- Seaborn
The analysis reveals several key insights:
- Top Earners: Host ID 2674028, 336950 and 1464510 are top 3 earners, indicating successful listing strategies.
- Monthly Earnings and Prices: A weak positive correlation (0.15) exists between monthly earnings and prices, suggesting that higher prices may contribute to increased earnings, but other factors also play a role.
- Maximum Bookings: "De Baarsjes / Oud West" and "Centrum West" are the neighborhoods with the most bookings, likely due to their attractive features and location.
- Price Differences Across Neighborhoods: There is a Significant price differences across neighborhoods. Which highlight the importance of location in pricing strategies.
- Quality and Price: A very weak negative correlation (-0.05) between overall satisfaction and price indicates that higher prices do not necessarily lead to better review scores.
- Price and Amenities: Moderate positive correlations between price and the number of accommodates (0.5) and bedrooms (0.45) suggest that listings with more amenities and space are priced higher.
These findings can help hosts optimize their listings and pricing strategies based on location, amenities, and guest preferences. They provide valuable insights for potential hosts on where to list their properties and how to enhance guest satisfaction.
High Level Design Document
Low Level Design Document
Architecture
WireFrame
Report
If you have any feedback, please reach out to me at Linkedin: https://www.linkedin.com/in/vineet-patel416/