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.