Airbnb Price Prediction using Hexaboost: A Data-Driven Approach

For both hosts and guests in the Airbnb ecosystem to make wise judgments, accurate price forecast is essential. This research suggests a novel method for predicting Airbnb listing costs that makes use of machine learning techniques. To train prediction models, the study incorporates a number of characteristics, including location, property type, amenities, and previous booking data. The dataset is pre-processed and valuable insights are extracted using feature engineering approaches. Mean absolute error and root mean square error are two relevant measures that are used during the training and evaluation of the models, which include gradient boosting, random forest, and linear regression. Moreover, ensemble techniques are used to combine the advantages of several models to improve forecast accuracy. The findings show that Airbnb's pricing predictions perform admirably, giving hosts insightful information to improve their pricing tactics and visitors’ information to help them make well-informed reservations. This study emphasizes the value of utilizing data-driven strategies in the sharing economy and adds to the expanding subject of short-term rental pricing prediction.