Airbnb Data Analysis of New York Listings

This mini project is based on the analysis of Airbnb listings in New York City using a real- world dataset. The main aim of this project is to study the data and discover useful patterns that can help improve business strategies for Airbnb hosts and the company itself. The dataset was taken from GitHub and includes details such as the listing price, room type, neighborhood group, number of reviews, availability, and other useful information.The tools used for this analysis were Jupyter Notebook and Tableau. In Jupyter Notebook, Python libraries like pandas, matplotlib, and seaborn were used for data cleaning, analysis, and visualization. For creating an interactive and easy-to-understand visual report, Tableau was used to design a dashboard.During the project, we explored several questions such as how listing prices are distributed, which types of rooms are most common, how listings are spread across different neighborhoods, the relationship between price and room type, and how the number of reviews has changed over time. These questions helped in finding valuable insights related to customer behavior, pricing strategies, and location preferences.In conclusion, this project gives a good example of how data analytics can be applied to real-world scenarios in the travel and hospitality industry. It also helps students learn the process of handling data, analyzing it, and presenting results in a clear visual format using modern tools.