Team
Dilshan Pelpola, Shrey Arora, Nikita Gaba, Hisham Bukhari
Categories
Artificial Intelligence, Machine Learning
Overview
Our Capstone project focused on developing a house price prediction model that leverages real estate data from the Toronto Regional Real Estate Board (TRREB). The core goal was to accurately predict property prices based on critical variables derived from extensive preprocessing of TRREB data, such as property type, location (postal codes), and average square footage. A significant part of our efforts went into cleaning, transforming, and engineering these features to ensure the model's accuracy and reliability. The project also includes a user-friendly web-based interface that integrates a map of Toronto, allowing users to interactively select a specific plot of land. Upon selection, the system uses the trained prediction model to generate a real-time price estimate for the chosen location. This streamlined solution combines robust machine learning and an intuitive UI, making it a valuable tool for stakeholders in the real estate industry.
Capstone Project - House Price Prediction
