In this competition, participants will use historical New York City real estate sales data to build a model that predicts the sale price of properties. The dataset contains information about building characteristics, locations, tax classifications, and previous assessments, along with common real-world data issues such as missing values and inconsistent formatting.

Your task is to preprocess the data, engineer meaningful features, and train a machine learning model that produces the most accurate price predictions for unseen properties. Submissions will be evaluated based on how closely the predicted prices match the actual sale prices.