This project was done as a requirement for the Udacity Nano degree program
- Pandas
- Numpy
- matplotlib
- Seaborn
- kepler-gl
My main motivations for analyzing the AirBnb dataset are
- Which neighbourhoods have the highest number of listings?
- Which neighbourhoods have the highest mean price per listing?
- What makes superhosts different?
- Why there was a fluctuation in the number of available listings over the year?
- medium_post_seattle.ipynb : contains the code used to analyze and produce the results used in the Blogpost
- Reamde: description of the repository
- License file
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Most of the listings in Seattle are located in the Central region which happens to be the Downtown and Capital Hill region
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Magnolia, Queen Anne, Downtown & West Seattle are most expensive neighborhoods.
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Cheap neighborhoods like Delridge, Northgate, Rainer Valley have higher ratings
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Number of Available listings dropped because more people were traveling Seattle in April through September
Kaggle for the dataset https://www.kaggle.com/airbnb/seattle/data
Python developers
Pandas developers
Seaborn developers
Matplotlib developers
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