This project aims to build a web-based application that predicts house prices based on various input features such as location, total square footage, number of bathrooms, balconies, and bedrooms. Using a trained machine learning model, the application provides an estimated price for a house in the specified location.
- Front-End: HTML, CSS, JavaScript, jQuery for dynamic form handling and AJAX requests.
- Back-End: Flask for handling HTTP requests and integrating the machine learning model.
- Machine Learning: A model trained using historical house price data to make accurate predictions.
- Template Engine: Jinja2 for rendering dynamic HTML content.
This project is ideal for users looking to estimate house prices quickly and efficiently based on specific features, providing valuable insights for both potential buyers and sellers in the real estate market.