Tableau Dashboard

Dataset: New York City Airbnb Open Data

The given dataset includes all information about the host, listed properties, geographical location, price reviews, and all other required metrics. Analyze the given dataset make different predictions and draw meaningful conclusions in order to grow the business. Also, state what can we learn from different predictions.

• What can we learn about different hosts and areas?
• What can we learn from predictions? (e.g.: locations, prices, reviews, etc.)
• Which hosts are the busiest and why?
• Is there any noticeable difference in traffic among different areas and what could be the reason for it?

This dataset has around 48,895 listings and 16 Columns. It is a mix between categorical and numeric values. Given dataset contains null values as blanks well we have to consider this while doing analysis. Last_review and reviews_per_month have more null values. There are 5 neighborhood groups in which all listings are located. Nearly 80-85% of listings are located in Manhattan and Brooklyn. In Manhattan, booking price is a bit higher as compared to other neighborhood groups. There are 3 kinds of room types (i.e. Shared Room, Private Room, Entire home/Apt). Out of which Shared room are least preferred by the customer even after having less price for booking.

By analyzing the given data set customer can make several decisions about their journey and the location. Customers could get an idea about expenses for the accommodation and which room to prefer in the particular area during the journey. Finding the perfect location for a night stay and the most selected Airbnb property according to previous customer reviews will be easy. This report can attract customers who want to plan a trip but have not visited that place before by checking the location and number of options available for the homestays. This report may increase reputation and company revenue growth along with the other businesses by increasing tourism.