https://github.com/ramyacp14/airbnb-analysis

A web-based application for geospatial analysis of Airbnb data, enabling users to visualize hotel locations on a map, and explore pricing and ratings through histograms, box plots, and scatter plots. Users can filter results by country, city, and price range to gain insights into hotel availability and pricing trends.
https://github.com/ramyacp14/airbnb-analysis

datavisualization folium geospatial-analysis matplotlib pandas plotly python seaborn streamlit

Last synced: 8 months ago
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A web-based application for geospatial analysis of Airbnb data, enabling users to visualize hotel locations on a map, and explore pricing and ratings through histograms, box plots, and scatter plots. Users can filter results by country, city, and price range to gain insights into hotel availability and pricing trends.

• Host: GitHub
• URL: https://github.com/ramyacp14/airbnb-analysis
• Owner: ramyacp14
• Created: 2024-09-06T21:43:03.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-06T21:46:21.000Z (about 1 year ago)
• Last Synced: 2025-01-13T08:46:23.929Z (9 months ago)
• Topics: datavisualization, folium, geospatial-analysis, matplotlib, pandas, plotly, python, seaborn, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 1.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
**Airbnb Geospatial Analysis**

This project is a web-based application that performs geospatial analysis on Airbnb data. The application allows users to select a country, city, and price range to view a map of available hotels, along with their prices and ratings. The application also provides exploratory data analysis (EDA) tools, including histograms, box plots, and scatter plots, to help users understand the distribution of prices and availability by country and city.

**Features**

* Geospatial analysis: View a map of available hotels in a selected country and city, along with their prices and ratings.

* Exploratory data analysis (EDA) tools:

	+ Histograms: View the distribution of prices by country and city.

	+ Box plots: View the distribution of prices by country and city.

	+ Scatter plots: View the relationship between price and availability by country and city.

* Filtering: Select a country, city, and price range to view relevant data.

* Hotel information: View detailed information about a selected hotel, including its price, rating, and availability.

**Technical Requirements**

* Python 3.8 or later

* Streamlit 1.10.0 or later

* Folium 0.12.1 or later

* Pandas 1.3.5 or later

* Matplotlib 3.5.1 or later

* Seaborn 0.11.2 or later

* Plotly 4.14.3 or later

**How to Run**

1. Clone the repository: `git clone https://github.com/your-username/airbnb-geospatial-analysis.git`

2. Install the required libraries: `pip install -r requirements.txt`

3. Run the application: `streamlit run app.py`

4. Open the application in your web browser: `http://localhost:8501`

**Acknowledgments**

This project was inspired by the Airbnb dataset from Kaggle. The code is based on the Streamlit library and uses Folium, Pandas, Matplotlib, Seaborn, and Plotly for data visualization.