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AirBNB

airbnb Data Analysis


About airbnb


Airbnb, Inc., founded in 2008 by Brian Chesky, Joe Gebbia, and Nathan Blecharczyk, is a global online marketplace connecting hosts offering accommodations and experiences with guests seeking unique stays worldwide. The platform has grown significantly, boasting over 8 million active listings across the world, more than 1,00,000 cities and 220 countries, with revenue reaching ₹ 3.73 billion

Airbnb is an online marketplace that connects people looking to rent out their homes with those seeking accommodations. Founded in 2008, the platform has grown into a global hospitality service that offers a wide variety of lodging options, including apartments, houses, villas, and even unique stays like treehouses and boats. Airbnb operates in more than 220 countries and regions, enabling travelers to experience local cultures by staying in residential neighborhoods rather than traditional hotels. The platform benefits both hosts, who can earn income by listing their properties, and guests, who often enjoy more personalized and cost-effective lodging. Airbnb also offers experiences and activities curated by local hosts, expanding its services beyond accommodation. Its user-friendly interface, secure payment system, and review-based trust system have made it a popular choice for travelers worldwide.

Learn more about airbnb CLICK HERE

About the datasets


Data set Contains 102599 Rows and Total 26 columns

Columns NAme are-
(['id', 'NAME', 'host id', 'host_identity_verified', 'host name', 'neighbourhood group', 'neighbourhood', 'lat', 'long', 'country', 'country code', 'instant_bookable', 'cancellation_policy', 'room type', 'Construction year', 'price', 'service fee', 'minimum nights', 'number of reviews', 'last review', 'reviews per month', 'review rate number', 'calculated host listings count', 'availability 365', 'house_rules', 'license']

column 'house_rules', 'license' have more than 50% data missing


Library Used

For handling the Data I used NumPy for matheatics Opreations, Pandas for handling the data , Matplotlib & Seaborn for visualization


What insight You will get

What is the distribution of listing prices?

How fiffernet room type are distributed

How are listings distributed across different neighborhoods?

About

To analyse the AirBNB data give a valuable insights that can help company to grow

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