This blog post is a write up about the project I have undertaken as part of Udacity Data Science nanodegree. The project is about data exploration and building a model for Starbucks rewards app data. Starbucks provided a dataset that contains simulated data. The dataset mimics customer behavior on the Starbucks rewards mobile app. According to the project’s introduction, once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free).
What property features influence the rental rate of homestays in Boston, MA?
This blog post is first project requirement of Udacity’s Data Science Nanodegree program. For this project, I chose to work on AirBNB Boston data. The dataset is publicly available through Kaggle. The dataset include the following:
1. Listings.csv— listing information, hosts information, review score. The listings file contain 3,585 records, 95 columns
2. Reviews.csv — Review comments The reviews file contain 68,275, 6 columns
3. Calendars.csv — availability and price for the day. The calendar file contain 1,308,890 records, 4 columns
In working to complete this project…