Kennung
Persistenter Bezeichner
Titel

U.S. Airbnb Open Data / 12331410

Beschreibungen
Abstract
he dataset used for this experiment consists of structured data where each row represents an individual Airbnb listing from the United States. The dataset contains approximately 50,000 rows and 15 columns, capturing detailed information about various Airbnb properties across different locations. Each row corresponds to a unique listing and includes features such as listing_id, host_id, city, property_type, room_type, price, number_of_reviews, and additional attributes that can potentially influence the listing price. The main objective of this experiment is to predict the listing price, which is a numeric and continuous variable, based on the provided input features. By utilizing various machine learning regression techniques, such as Random Forest Regressor or XGBoost, the goal is to model the relationships between the property features and the final listing price accurately. Preprocessing steps including handling missing values, encoding categorical variables, and outlier removal will be applied to ensure high data quality. The predictive models will be evaluated based on metrics such as Mean Squared Error (MSE) and R-squared (R²), ensuring robust and interpretable results.
Herausgeber
TU Wien
Schöpfer

Veröffentlichungsdatum
2025
Zitierempfehlung
Motoren