This project aims to build a predictive model for estimating Airbnb rental prices
based on listing features such as location, room type, amenities, and host characteristics.
The problem addresses the need for accurate, data-driven pricing to help hosts set
competitive rates and assist users in understanding market trends. The target audience
includes Airbnb hosts, property managers, and students learning machine learning
applications. The project will use Python-based data preprocessing, feature
engineering, and an XGBRegressor model to achieve strong predictive performance
and interpret key pricing factors.
