# Airbnb Business Analytics: NYC Insights with Orange Data Mining
> Explore NYC Airbnb listing insights using clustering, regression, and data mining. Learn key pricing factors and business strategies for hosts and investors.

Tags: airbnb-analytics, data-mining, orange-software, business-intelligence, market-analysis, nyc-data, k-means-clustering, predictive-modeling
## Academic Project Overview
An analysis of the NYC Airbnb marketplace using Orange Data Mining tools to generate business intelligence for property investors and hosts.

## Dataset & Key Variables
*   **Source:** NYC Airbnb Open Data (millions of listings).
*   **Key Variables:** Price, Neighborhood Group, Room Type, Availability/Capacity (Ava Cap), Number of Reviews, and Minimum Nights.

## Analytical Methods Applied
*   **Clustering (K-Means):** Segmented data into Premium (high price/low availability), Moderate, and Budget (low price/high availability) groups.
*   **Regression Analysis:** Identified that location in Manhattan and 'Entire Home' property types are the strongest predictors of high prices.
*   **Association Rule Mining:** Found that entire homes consistently command higher prices, while higher availability often correlates with lower demand/pricing.

## Key Findings
*   Manhattan listings are significantly more expensive than other boroughs.
*   Private rooms are the most common listing type.
*   There is a weak correlation between the number of reviews and the listing price.

## Business Recommendations
*   **Dynamic Pricing:** Implement rates based on demand and location.
*   **Listing Optimization:** Use better imagery and service descriptions to increase visibility.
*   **Incentives:** Offer lower nightly rates for longer stays and discounts during off-peak periods.
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