Analyzing Airbnb data for Boston and Seattle

Descriptive analysis of Airbnb data from Seattle, Boston

Used libraries:

• collections
• matplotlib
• numpy
• pandas
• scipy
• seaborn
• sklearn
• warnings

Version:

• Python 3.7
• Anoconda 1.9.7
• Jupyter 6.0.0

CRISP-DM

Business Understanding: This data contain a lot of parameters related to Boston and Seattle Airbnb data.

Three Queations

1. I will use the data to find out some main factors that affect the price
2. Find a good model to predict the socre
3. What should a host do to improve the score(review_scores_rating) of his house from this model；

Files

Major file is a Notebook for Analyzing airbnb data for Boston and Seattle.

finalized_model.sav: the decision tree regressor we obtained with all features

deploy_model.sav: model with only 9 features

airbnb_regressor: dot file that shows structure of decision tree regressor