rendeer / write_a_data_science_blog Goto Github PK

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

The development tool of this project is Jupyter notebook and the Anaconda distribution of Python 3.7. Other necessary python libraries are listed below:

• pandas 0.24.2
• numpy 1.16.4
• matplotlib 3.1.0
• seaborn 0.9.0
• scikit-learn 0.21.2
• folium 0.10.0

In this project, I was interestested in researching the relationship between geo-location and price using the seattle airbnb data by trying to answer following questions:

• How many clusters can we get using the lat, lon values in the Dataset?
• Is the geo-clusters strongly correlated with the price?
• During prediction of the price, would the feature 'geo cluster' be helpful?

seattle: Contains the Seattle Airbnb dataset CSV files.

airbnb_seattle.ipynb: Jupyter notebook with python code performing the dataset analysis.

The main findings of the code can be found at the post available here.

This project used AIRBNB Seattle dataset and it was obtained from here