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https://github.com/stefagnone/airbnb-data-analysis
Data analysis and visualization of Airbnb listings using text mining frameworks, Tableau dashboards, and MongoDB to uncover business insights for optimizing strategies.
https://github.com/stefagnone/airbnb-data-analysis
airbnb-data-analysis business-insights data-visualization mongodb r-programming sentiment-analysis tableau-dashboards text-mining
Last synced: 17 days ago
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Data analysis and visualization of Airbnb listings using text mining frameworks, Tableau dashboards, and MongoDB to uncover business insights for optimizing strategies.
- Host: GitHub
- URL: https://github.com/stefagnone/airbnb-data-analysis
- Owner: stefagnone
- Created: 2024-12-13T08:45:41.000Z (23 days ago)
- Default Branch: main
- Last Pushed: 2024-12-13T09:03:19.000Z (23 days ago)
- Last Synced: 2024-12-13T09:31:21.249Z (23 days ago)
- Topics: airbnb-data-analysis, business-insights, data-visualization, mongodb, r-programming, sentiment-analysis, tableau-dashboards, text-mining
- Language: R
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Airbnb Data Analysis and Visualization Project
## Project Overview
This project involves analyzing Airbnb data to extract actionable business insights from textual and numerical content. By leveraging text mining frameworks and data visualization tools, this project highlights trends, host behaviors, and customer preferences in the Airbnb ecosystem. The findings aim to help Airbnb optimize their listing strategies, enhance guest satisfaction, and address key market dynamics.
### Key Highlights:
- **Text Mining**: Analyzed Airbnb listings using sentiment analysis, bigram frequency analysis, and TF-IDF to uncover key descriptors and sentiments.
- **Data Visualization**: Created insightful visualizations using Tableau, illustrating trends like Airbnb density, price distributions, host performance, and cancellation policies.
- **Business Insights**: Delivered actionable recommendations for inventory management, pricing strategies, and policy improvements.
## Technologies Used
- **R Programming**: Text mining frameworks and connection to MongoDB.
- **Tableau**: Interactive dashboards and visualizations.
- **MongoDB**: Data storage and retrieval for Airbnb data.
- **Python**: Data cleaning and additional analysis.
## Repository Structure
```plaintext
|-- Code/
|-- MongoDB_Airbnb_in_R.R # R script to connect to MongoDB and structure Airbnb data
|-- MongoDB_Airbnb_in_R_Stefano.R # Custom R script for text mining and analysis
|-- Data/
|-- Mongo_DB_Setup.pdf # Instructions for setting up MongoDB for Airbnb data
|-- Output/
|-- Tableau_Stefano_Compagnone.twbx # Tableau workbook containing visualizations
|-- Key Findings_Report.docx # Report detailing key insights and findings
|-- Screenshots/
|-- Airbnb_Visualizations.docx # Screenshots of Tableau visualizations
```
## Key Insights
- **Sentiment Trends**: Listings exhibit predominantly positive sentiments, emphasizing trust and happiness, which serve as key differentiators for Airbnb's brand.
- **Customer Preferences**: Guests prefer entire homes/apartments with real beds, highlighting the demand for comfort and privacy.
- **Cancellation Policy Trends**: Most hosts prefer strict policies, which ensure booking security but may deter flexibility-seeking guests.
- **Listing Features**: Hosts emphasize location and accommodation type in their listings, resonating with guest priorities.
## Instructions
1. Deploy a free MongoDB cluster and load the "Airbnb" sample data.
2. Use the provided R scripts to connect to MongoDB and preprocess the data.
3. Open the Tableau workbook (`Tableau_Stefano_Compagnone.twbx`) to explore visualizations.
4. Review the report and screenshots in the `Output/` directory for a summary of findings.
## Contact
Feel free to reach out for any queries or feedback:
**Stefano Compagnone**
[[email protected]](mailto:[email protected]) | +1 617-251-3853