Using Seattle Airbnb Open Data in Kaggle we can investigate and find how to help hosts on Airbnb to gain more revenue. On the other hand we can improve the satisfaction of the guests by improving the review ratings. We will try to answer the following business questions.
- How to gain more income from listing your property in Seattle?
- How to get better review ratings?
- How is the demand for Airbnb properties in Seattle?
- Jupyter Notebook of the study contains the following.
- Exploring and Understanding Data.
- Handling Missing Values.
- Handling Date Columns.
- Handling Numerical Features.
- Handling Categorical Features.
- Preparing Data for Modling.
- Evaluating the Model.
- Jupyter Notebook Draft for exploring data features.
- Data set downloaded from Kaggle conctains the following files.
- Listings, including full descriptions and average review score
- Reviews, including unique id for each reviewer and detailed comments
- Calendar, including listing id and the price and availability for that day
The following Python libraries used in this study.
- Numpy
- Pandas
- Matplotlib
- Datetime
- Seaborn
- sklearn
- The location is not the main factor that affects the price of a listing on Airbnb, but the needs of the guest or a special experience offered to the guest can make higher revenue.
- Better review ratings can lead the host to boost the revenue by pursuing the satisfaction of the guest. Being a super host and fast response time make difference in the review ratings.
- Demand on the properties has a pattern over the year, and this can help us understand, how to get the highest demand. Other factors like the property type and payment policy increase the demand for the properties.
The findings and explaination of the study in the following blog. https://bassemessam-10257.medium.com/how-to-gain-more-income-from-airbnb-e251be77524a
This study is a part of a project in Udacity Data Scientist Nanodegree.