Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Boston, MA.
This project requires Python 3.x and the following Python libraries installed:
- NumPy
- Pandas
- matplotlib
- scikit-learn
- Jupyter-Notbook
Or you could install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
Using this dataset I tried to answer these three questions:
- What are the major factors that influence the price of an Airbnb rental in Boston?
- Which neighborhoods in Boston have the highest rental prices?
- What time of year has the highest rental prices?
Dataset used in the analysis is in these two files:
Listings, including full descriptions and average review score.Calendar, including listing id and the price and availability for that day.
Analysis is in: Boston Airbnb Data Analysis.ipynb file and saved as html file in Boston_Airbnb_Data_Analysis.html.
In a terminal or command window, navigate to the top-level project directory Boston-Airbnb-Data-Analysis / (that contains this README) and run the following command:
jupyter notebook Boston_Airbnb_Data_Analysis.ipynb
Blog post discuss the results is available here
This project is licensed under the MIT License - see the LICENSE file for details