This project explores the 2019 Airbnb dataset for New York City to uncover market trends, pricing insights, and neighborhood patterns. The workflow involves data cleaning, visualization, and statistical analysis to answer key questions about the Airbnb market.
Loaded the dataset AB_NYC_2019.csv using pandas.
Inspected data types, missing values, and summary statistics.
Cleaned the data by:
Removing missing entries for crucial fields like neighbourhood_group, room_type, and price.
Filtering out extreme prices (kept listings under $1000 to focus on realistic values).
Several visualizations were created with Matplotlib and Seaborn to highlight major trends:
Price Distribution → Most listings are in the affordable range, with a few high-end outliers.
Average Price by Borough → Manhattan consistently shows the highest average prices, while Staten Island and Bronx are more budget-friendly.
Room Type Distribution → “Entire home/apartment” and “Private room” dominate the market.
Price by Room Type & Location → Entire homes/apartments in Manhattan command the highest rates.
Most Listings: Brooklyn and Manhattan have the largest share of Airbnb listings.
Most Common Room Type: Entire homes/apartments, followed by private rooms.
Neighborhood Patterns: Listings are concentrated in well-known, tourist-friendly neighborhoods.
Average & Median Prices: Prices vary significantly across boroughs and room types.
Neighborhood Extremes:
Most Expensive: Woodrow (Staten Island), Whitestone (Queens), and Tribeca/SoHo (Manhattan).
Most Affordable: Graniteville (Staten Island), Van Nest (Bronx), and Dyker Heights (Brooklyn).
Seasonality: Availability data (availability_365) gives an indication of year-round rental patterns.
âś… Key Takeaway: Manhattan is the priciest and most competitive Airbnb market, while outer boroughs like Bronx and Staten Island offer budget-friendly alternatives. Entire apartments dominate demand, but private rooms make up a large, accessible segment.