http://insideairbnb.com/new-york-city/,
https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data
Airbnb is an American online marketplace for vacation rentals, headquartered in San Francisco, California. The platform, accessible via its website or app, allows users to book lodging—primarily homestays—and tourism experiences, or to list their properties for rental. Unlike traditional property owners, Airbnb does not own the properties listed; instead, it earns revenue by taking a commission on each booking. Founded in 2008 by Brian Chesky, Nathan Blecharczyk, and Joe Gebbia, Airbnb's name is derived from its original title, AirBedandBreakfast.com.
The company has faced criticism for driving up rent prices in cities where it operates and causing disruptions for residents living near rental properties. This has led to increased regulatory scrutiny from cities like San Francisco and New York City, as well as the European Union. Additionally, Airbnb has encountered opposition from the hotel industry and competing businesses.
New York, New York—isn’t it everyone’s favorite city? Tourism plays a crucial role in the city's economy, and when planning a vacation or trip to this iconic destination, the first things we usually consider are accommodation options like hotels, motels, or Airbnbs. Recently, Airbnbs have been gaining popularity and trust among travelers because they offer a more homely and personalized experience, even in an unfamiliar city. This sense of comfort and familiarity is one of the key reasons why Airbnbs are increasingly preferred over traditional hotels and motels. Additionally, Airbnbs often tend to be more affordable, making them an attractive option for budget-conscious travelers. The combination of cost-effectiveness and a cozy, home-like atmosphere is helping Airbnbs carve out a significant niche in the competitive accommodation market.
This project aims to analyze and visualize the factors influencing Airbnb pricing through Exploratory Data Analysis (EDA), helping property owners set competitive prices. By identifying key variables that impact pricing, we will develop a machine learning model to predict Airbnb prices, enabling data-driven decision-making for optimizing listings. Additionally, the project will include sentiment analysis of guest reviews to uncover positive and negative feedback. This analysis will provide valuable insights for owners to enhance the guest experience and improve the appeal of their properties, ultimately driving better performance in the marketplace.
This dataset describes the airbnb lisiting activity of home-stays in New York.
Data Collection
Data Cleaning
Exploratory Data Analysis
Data Modeling
Modeling on Price Data
Model Evaluation
Sentiment Analysis
Conclusion
References