https://github.com/mr-chang95/datascience_airbnb

Data Science Project for Udacity's Data Scientist Program. Using Python in Jupyter Notebook.
https://github.com/mr-chang95/datascience_airbnb

airbnb data-analysis data-science data-visualization jupyter-notebook numpy pandas python sklearn

Last synced: 4 days ago
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Data Science Project for Udacity's Data Scientist Program. Using Python in Jupyter Notebook.

• Host: GitHub
• URL: https://github.com/mr-chang95/datascience_airbnb
• Owner: Mr-Chang95
• Created: 2022-01-17T21:37:10.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-14T03:35:37.000Z (about 2 years ago)
• Last Synced: 2024-11-28T04:14:37.518Z (2 months ago)
• Topics: airbnb, data-analysis, data-science, data-visualization, jupyter-notebook, numpy, pandas, python, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.83 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Udacity Data Science NanoDegree - Airbnb Project

![house](https://user-images.githubusercontent.com/92649864/149861946-304ca545-54b5-45d0-93b1-261c80d81f28.png)

## Motivation

For this project, I am interested in using the 2016-17 Seattle and Boston Airbnb datasets to answer the following questions: 


    - What are the important amenities of these listings? Compare the two cities. 


    - Is it possible to predict the price with 5 features? If yes, compare the 2 cities. 


    - How does the price in each city change each month? Be sure to compare the 2 cities. 


    - How does the total number of listings change each month? Be sure to compare the 2 cities. 


I chose not to include the .csv file because I would like the extra practice of using and writing in the .gitignore file.

## File Descriptions

You will find 4 Jupyter Notebook files in this repository. Part I mainly deals with examining and understanding the datasets. I have also answered some basic questions about the datasets in this part.

The next 3 parts deal with answering the questions that I have posed. Most of them require a bit of legwork before I could answer them.

The catboost_info folder is related to the model I tested in Part III.

## Packages

List of packages used:

~~~~~

- Matplotlib

- Numpy

- Pandas

- Calendar

- Seaborn

- Scikit-learn

~~~~~

## Medium Article

Medium Article Link: https://medium.com/@mr.dcny/a-study-of-airbnb-listings-seattle-boston-ff3a69646edf

## Acknowledgements

Special thanks to Udacity for this Data Science course project. I would also like to thank Airbnb and Kaggle for providing these datasets.

Here are the links to the datasets: 


   - https://www.kaggle.com/airbnb/seattle/data 


   - https://www.kaggle.com/airbnb/boston