Cost Prediction for Home Construction Using Quantum Computing

Creating a reliable model for predicting house construction costs means looking at lots of different information about building houses and seeing how prices have changed over time. Our goal is to create a system that explores the application of the random forest algorithm for housing construction cost prediction. Additionally, we investigate the potential integration of quantum computing techniques to improve the efficiency and accuracy of cost estimation. This hybrid approach leverages quantum algorithms to optimize complex cost prediction models efficiently. It can be achieved based on numerous attributes, including area, number of rooms and number of bedrooms, building materials, labor costs, and construction time. This system cleans the data, scales the features, and trains using a representative dataset, while also exploring quantum-assisted preprocessing techniques for data optimization.