The project intends to identify and understand the underlying patterns in Airbnb dataset which describes the listing activity of homestays in New York. Vacation rentals, also called short-stay rentals, are booming in New York. As analysts who also travels and hosts on Airbnb, we wanted to know what the market looks like in New York, how other hosts are doing, the most popular locations, amenities.
Below are some of the major questions to which we are trying to find answers to by analyzing the data:
· What are major factors influencing or will help in deciding the prices of the listings? · How does the demand of Airbnb rentals has changed over time? · What kind of listings (hosts) are favored by the customers?
Through the insights generated from the analysis we are hoping to provide valuable information to both the business and the vacation rental community regarding the following:
· Improving the pricing of a new listing · Help in understanding the favorable qualities of a host and get listed as one · Help in choosing the best hosts or best time of vacation
Dataset Description:
This dataset has around 49,000 observations in it with 16 columns and it is a mix between categorical and numeric values. Table 1 mentions few of the attributes and their descriptions. The dataset has been obtained from https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data. ID of the data set provides the unique number for every single entry in the Airbnb. Host_Id of the data set provides the unique number provides the unique number for every host in the New York city. Name lists the name of the listing and Host_name provides the name of the host.