ahomer/airbnb_bst

CRISP-DM processing using python scripts on Boston data of airbnb.

Boston-data

This is about the airbnb Boston-data data analysis.You can download data from https://www.kaggle.com/airbnb/boston. I had downloaded it and saved it in 'boston-airbnb' folder.

You can find some interesting opinions about:

• What is the most expensive season in Boston?
• What are the top factors strong relation to price?
• How to predict price？
• What will lead to bad impression？

Prerequisites

You need to install python,such as python 3 from https://www.python.org/. Then you should install these packages listed below using pip:

pip install matplotlib
pip install pandas 
pip install langdetect 
pip install nltk 
pip install jupyter 

Getting Started

Just clone the the repo and open the notebook file (boston-airbnb.ipynb). Go through the notebook and take a data analysis journey.

Content

The analysis process follow the CRISP-DM

1. Business understanding
2. Data understanding
3. Data preparing
4. Modeling
5. Evaluation
6. Deployment

More interesting opinions,please go to https://medium.com/@bruce007/exploration-on-boston-data-of-airbnb-5a63da751366

Acknowledgements

Thanks to Aleksey Bilogur(https://www.kaggle.com/residentmario/modeling-prices). His modeling price is very effective. Thanks to Aleksandra Deis(https://www.kaggle.com/aleksandradeis/airbnb-seattle-reservation-prices-analysis). I read her post before starting this job.

License

This project is licensed under the terms of the MIT license. See LICENSE for additional details.