edwinhung / airbnb_seattle Goto Github PK

1. Installation
2. Project Motivation
3. Dataset
4. File Descriptions
5. Statistical Models
6. Result
7. Acknowledgements

All libraries used in the code should be in Anaconda distribution of Python 3.8.

I am interested in learning insights about recent local travel and leisure activities from Airbnb Seattle listing. Specific Question includes:

1. What factors would affect listing price?
2. Are these factors as influential as what tourists/residents expect?
3. Is there pattern in price trend throughout the year?
4. How statistical model perform on predicting price? and what insights can we get from model?

Two datasets used here are Airbnb Seattle listing and calendar dataset downloaded from Inside Airbnb with collection date of 02/21/2021. If you are interested in more datasets from Seattle or other cities, please see the link Here.

There is one Jupyter notebook that includes code and output related to questions above. Markdown cells show title of each section. Comments in code are used to help readers understand specific steps performed in code.

Linear Regression and Random Forest are used so to see how simple and complex models perform on listing dataset.

Findings of this project was published on Medium available here.

Source of datasets is Inside Airbnb which can be found here. A portion of codes were borrowed from chapter 2 of Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron. This is also my first project done with Udacity Data Scientist nanodegree.