# Airbnb Business Analytics with Orange Data Mining
> Discover how to use Orange Data Mining for Airbnb market analysis, including K-Means clustering, regression, and pricing strategies for NYC listings.

Tags: data-mining, business-analytics, orange-data-mining, airbnb-dataset, market-analysis, k-means-clustering, nyc-data
## Business Analytics Using Orange Data Mining
- Applied business intelligence methods to the NYC Airbnb Open Data set.
- Technologies used: Orange Data Mining tool.

## Dataset & Variables
- Dataset covers millions of NYC listings, including pricing, availability, and customer behavior.
- Key variables: Price, Neighborhood Group, Room Type, Capacity, and Number of Reviews.

## Data Preprocessing
- Removed unnecessary variables (IDs, host names) and outliers (>1000).
- Replaced missing values and normalized numerical data for consistent analysis.

## Analytical Methods & Results
- **Clustering (K-Means):** Segmented listings into Premium (high price, low availability), Moderate (largest segment), and Budget (high availability).
- **Regression Analysis:** Identified Manhattan location and 'Entire Home' status as the strongest predictors of high price.
- **Association Rule Mining:** Found that entire home listings command higher prices, while higher availability correlates with lower prices.

## Key Findings & Recommendations
- Listings in Manhattan are significantly more expensive than other boroughs.
- Recommended strategies: Dynamic pricing, listing whole houses in high-demand areas, and optimizing descriptions/images.
- Conclusion: Pricing is primarily driven by location and property type rather than customer reviews.
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