This project dives deep into Airbnb listings in New York City (2024) using Python to uncover key insights through Exploratory Data Analysis (EDA) and data visualization. The goal is to analyze pricing trends, neighborhood distribution, and room availability to help hosts optimize their listings and travelers find better deals.
✅ Data Cleaning & Preprocessing – Handling missing values, removing duplicates, and type conversions.
✅ Feature Engineering – Creating new insights such as price per bed for better analysis.
✅ Price Distribution & Outlier Analysis – Identifying unusual pricing patterns.
✅ Neighborhood & Room Type Analysis – Exploring price variations across boroughs.
✅ Geo-Spatial Visualizations – Mapping Airbnb locations using latitude & longitude.
✅ Correlation Heatmaps & Trend Analysis – Understanding relationships between key factors.
✅ Interactive Visualizations – Using Matplotlib & Seaborn to create impactful charts.
- Python 🐍
- Pandas & NumPy 📊 (Data Cleaning & Processing)
- Matplotlib & Seaborn 📈 (Data Visualization)
- Geospatial Analysis 🗺️ (Mapping listings across NYC)
The dataset includes Airbnb listings from NYC for 2024 with details such as:
📌 Listing ID, Host Name, Neighborhood, Room Type, Price, Availability, Reviews, etc.
1️⃣ Clone this repository:
git clone https://github.com/arundeepp9393/Airbnb-NYC-EDA.git2️⃣ Navigate to the project folder:
cd Airbnb-NYC-EDA3️⃣ Install required libraries:
pip install pandas numpy matplotlib seaborn4️⃣ Run the Jupyter Notebook or Python script:
jupyter notebook Airbnb_EDA.ipynb🔹 Manhattan has the highest Airbnb prices, while Bronx & Queens offer budget-friendly stays.
🔹 Private rooms are cheaper, but entire apartments dominate the market.
🔹 Some extreme outliers were found in pricing (over $10,000 per night).
🔹 Hosts with multiple listings have different pricing strategies.
🔹 Availability varies greatly based on location & seasonality.
📌 Price Distribution & Outliers – Boxplots & Histograms
📌 Neighborhood Comparisons – Bar Charts & Pie Charts
📌 Geo-Spatial Analysis – Scatter Maps of Listings
📌 Heatmaps – Correlation between price, availability & reviews
This analysis helps Airbnb hosts optimize pricing strategies and travelers find budget-friendly stays in NYC. The insights can be used for investment decisions, pricing optimizations, and tourism planning.
📌 Machine Learning – Predicting Airbnb prices based on location & features.
📌 Interactive Dashboards – Using Power BI or Plotly Dash for better visualization.
📌 Time-Series Analysis – Studying seasonal trends in Airbnb pricing.
This project is open-source and free to use under the MIT License.
- Linkedin: linkedin.com/in/arun 🌐
- GitHub: github.com/ArunCooksData 👨💻
- Email: arundeepp9393@gmail.com 📧
✨ If you find this project helpful, don't forget to ⭐ the repo!