What makes me to perform this analysis on Seattle Airbnb? People love travelling and I am not exception. in the past, people mostly lived in hotels or hostels unless you have friends or relatives in that place.
Now, we have Airbnb which provides an alternative way for our accommodation. Not only can we be traveller, we can also be the host to provide accommodation to others.
What I want to find out in this analysis is how Airbnb is growing in a city (Seattle) and if more and more people are using the platform to find accommodations. How to become successful in Airbnb business is another thing I want to find out.
There are 3 main questions I hope to answer with this data set.
- What are the busiest times of the year to visit Seattle? How will the price react to it?
- Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?
- How to be successful in Airbnb business?
The source is from https://www.kaggle.com/airbnb/seattle
3 python libraries are required to run the analysis
- numpy
- pandas
- matplotlib
If they are not yet installed, you can use pip to install
pip install numpy
pip install pandas
pip install matplotlibJupyter notebook "Seattle Airbnb Analysis.ipynb"
- To run the analysis, clone the repository
- 3 csv files have to be downloaded from https://www.kaggle.com/airbnb/seattle and put in the directory "seattle"
- calendar.csv
- listings.csv
- reviews.csv
- Open the .pynb Jupyter Notebook file and execute the cells.
- Jul-2016 was the busiest period in Seattle and the price for Airbnb was the highest.
- There was an upward trend on both Airbnb listings and visitors
- To be more successful, superhost status is required. A higher rating should be achieved by being more responsive.
- Use plot.ly, seaborn to create attractive visualizations
- Add more analysis