This repository contains a Jupyter Notebook where the Airbnb data for Seattle has been analyzed. A blogpost on the same subject can be found here:
The code runs using Python version 3.7. Necessary libraries are:
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Scikit-learn
You can install any missing libraries using pip:
pip install pandas numpy matplotlib seaborn scikit-learn
The project aims to analyze Airbnb data for Seattle to answer the following questions:
How do the rental prices in Seattle change depending on the time of the year?
How does the availability of Airbnb accommodations change in Seattle throughout the year?
How are some relevant details about the Airbnb correlated with the price of the rental?
The motivation behind the project is to provide insights to potential Airbnb users in Seattle so they can make more informed decisions regarding their accommodation.
This repository contains a Jupyter Notebook (workbook.ipynb), which includes all the exploratory data analysis, preprocessing, visualizations necessary to answer the project's questions.
The raw data files are not included in the repository due to their large size, but can be obtained from Kaggle.
https://www.kaggle.com/code/aleksandradeis/airbnb-seattle-reservation-prices-analysis
To interact with the project, you should:
Clone this repository.
Ensure you have all the necessary Python libraries installed.
Download the dataset and place it in the same directory as the cloned repository.
Open workbook.ipynb in Jupyter Notebook.
You can now interact with the data, modify the code, or run the cells to see the results of each operation.
This project is licensed under the MIT License - see the LICENSE.md file for details. The dataset used for the analysis is provided by Airbnb.
Author: Chirag Garg
Acknowledgements: I would like to thank Airbnb for providing the dataset that made this analysis possible. Also, I am grateful to the online data science community for the plethora of resources available for learning and troubleshooting.