UdacityProject1

Project Motivation

This project was completed to satisfy requirements for Udacity Data Science nano degree. The project is aimed at answering a few questions that could help an AirBnB host in the Seattle, WA area better manage their property based on usage data from 2016. The following questions were answered.

1. Are total listings similar each week day?
2. Are total listings similar each month?
3. Does Utilization vary by weekday?
4. Is Utilization the same for each month?

A blog post associated with this project can be found here

Libraries used

Numpy and pandas for data cleaning and wrangling, seaborn for visualizations

Files

Project1.ipynb : jupyter notebook showing data analyzed and plots generated for each business question along with the codes used. README.md : This current file calendar.csv : contains AirBnB listings for each day and a column indicating whether it was available or not

I worked on the AirBnB Seattle,WA listings and calendar data. I answered the following questions in the Project1.ipynb jupyter notebook

Summary of the Analysis

Question 1 : Are the total number of listings similar each week day?

# Yes

Question 2 : Are the total number of listings similar each month?

# Yes

Question 3 : Does Utililzation vary by weekday?

# No

Question 4 : Is Utilization the same for each Month?

# No. January has the highest Utilization and December the least.

Acknowledgement

The data used on this project can be found on Kaggle. Thanks to both AirBnB and Kaggle for making this data available. Thanks to Python developers who developed the libraries used on this project and everyone else that contributed to the Open Source tools used.