The AirBNB Analysis project provides insights into various listings on AirBNB, utilizing data analysis techniques to explore trends, pricing, and host characteristics in the Paris area. This analysis is performed using a Jupyter Notebook.
The analysis includes:
- Exploratory Data Analysis (EDA) to understand the distribution of prices and ratings.
- Visualization of trends in host response rates and acceptance rates.
- Correlation analysis between various features.
The dataset includes various fields such as:
- listing_id: Unique identifier for each listing.
- host_id: Unique identifier for the host.
- neighbourhood: The neighborhood where the listing is located.
- price: Price per night.
- review_scores: Ratings across multiple categories.
- Average Pricing: The average price per night is approximately €113, with prices ranging from €0 to €12,000, indicating a wide variability in listing costs.
- Host Characteristics: Listings managed by superhosts tend to receive higher ratings and may attract more bookings.
- Popular Neighborhoods: Buttes-Montmartre has the highest number of listings (7,232), suggesting it is a popular area among guests.
- Guest Capacity: Most listings accommodate an average of 3 guests, which aligns with typical travel groups.
- Optimize Pricing: Consider adjusting prices based on seasonal demand and local events to maximize occupancy rates.
- Enhance Guest Experience: Focus on improving communication and response times, as higher host response rates correlate with better guest satisfaction.
- Highlight Unique Features: Listings with distinctive amenities or experiences tend to attract more interest; consider showcasing these in your listing description.
- Leverage Reviews: Encourage guests to leave reviews and respond to feedback to build credibility and improve future bookings.
https://www.kaggle.com/code/mahalaxmiwadeyar/notebook27341ebd87