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🏠 Airbnb Data Analysis

Python Pandas Matplotlib Seaborn Platform Status

An Exploratory Data Analysis (EDA) project on Airbnb listings across New York City using Python, Pandas, and Matplotlib. Analyzes pricing trends, neighbourhood patterns, room type distributions, host activity, cancellation policies, and availability insights across 102,599 listings from 2003 to 2022.


🎯 Objective

  • Analyze pricing trends across NYC boroughs and room types
  • Identify top hosts, neighbourhoods, and listing patterns
  • Understand how cancellation policies affect availability
  • Explore correlations between reviews, pricing, and host verification
  • Derive actionable insights from 102,599 listings spanning 2003–2022

📁 Dataset

Property Value
Records 102,599
Columns 26
Coverage 2003 – 2022
Country United States
Price Range $50 – $1,200
Fields ID, Name, Host ID, Host Name, Host Verified, Neighbourhood Group, Neighbourhood, Latitude, Longitude, Country, Instant Bookable, Cancellation Policy, Room Type, Construction Year, Price, Service Fee, Minimum Nights, Number of Reviews, Last Review, Reviews per Month, Review Rate, Host Listings Count, Availability 365, House Rules, License

🛠️ Tech Stack

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Google Colab / Jupyter Notebook

📂 Project Structure

airbnb-data-analysis/
├── Airbnb Dataset.xlsx             # Dataset file
├── Airbnb_Data_Analysis.ipynb      # Main analysis notebook
├── requirements.txt                # Python dependencies
└── README.md

📊 Analysis & Visualizations

Chart Description
Room Type Distribution Count of listings across 4 types — Private Room, Entire Home/Apt, Shared Room, Hotel Room
Listings by Neighbourhood Group Manhattan (43,792) and Brooklyn (41,842) dominate the NYC listing count
Average Price by Neighbourhood Group Bar chart comparing average nightly price across all 5 NYC boroughs
Top 5 Most Active Hosts Michael leads with 881 listings, followed by David (764) and John (581)
Price Distribution Histogram Frequency of prices ($50–$1,200) with median marker at $624
Min Nights by Room Type Average minimum stay required per room type
Price vs. Review Rating Average price grouped by review score (1–5)
Availability by Cancellation Policy How strict / moderate / flexible policies affect days available per year
Price Trend by Construction Decade Line chart of average price across property build years (2003–2022)
Price by Host Verification Status Verified vs. unverified host pricing comparison
Price vs. Reviews Per Month Scatter plot showing correlation between review frequency and price
Top 5 Most Expensive Neighbourhoods Neighbourhoods with highest average price (min. 10 listings)

🔍 Key Insights

  • Manhattan has the highest number of listings (43,792) and commands the highest average nightly prices among all NYC boroughs
  • Entire Home/Apt is the most common room type, followed closely by Private Room
  • Michael is the most active host with 881 listings — a small group of power hosts dominates the platform
  • Price range spans $50 to $1,200 with a median of $624, indicating a right-skewed distribution with premium outliers
  • All listings are based in the United States, specifically New York City across 5 borough groups
  • Flexible cancellation policies are associated with significantly higher annual availability compared to strict policies
  • Guest review ratings range from 1 to 5, with most listings clustering at 4–5 stars

🚀 Run the Project

git clone https://github.com/Subi121/airbnb-data-analysis.git
cd airbnb-data-analysis
pip install -r requirements.txt
jupyter notebook Airbnb_Data_Analysis.ipynb

Or open directly in Google Colab:

  1. Open the notebook Airbnb_Data_Analysis.ipynb in Google Colab
  2. Upload Airbnb Dataset.xlsx
  3. Run all cells to generate charts and insights

🔭 Future Improvements

  • Sentiment analysis on listing names and house rules
  • Predict listing price using ML regression models (Ridge, XGBoost)
  • Build an interactive dashboard using Plotly or Streamlit
  • Add geospatial heatmap of listings and pricing using Folium

⚠️ Disclaimer

  • This is an independent data analysis project completed during an internship at VOIS (Vodafone Intelligent Solutions).
  • Not affiliated with or endorsed by Airbnb, Inc.
  • Dataset was provided as part of the internship program

📄 License

This project is licensed under the MIT License.

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Exploratory Data Analysis on NYC Airbnb listings — uncovering pricing trends across 5 boroughs, host activity patterns, and availability insights across 102,599 listings using Python and Seaborn.

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