Live Link and Output Overview

Live Demo: House Price Prediction System

After training and evaluating the XGBoost model, the House Price Prediction System can be deployed as an interactive application using tools such as Gradio or Streamlit.

Users can provide housing information such as median income, house age, average number of rooms, population, and geographical location. The trained model processes these inputs and instantly predicts the estimated house value.

The prediction workflow includes:

1. Accepting user inputs through the interface.
2. Preprocessing the input data.
3. Passing the inputs to the trained XGBoost model.
4. Generating a predicted house value.
5. Displaying the result to the user.

This confirms that the complete machine learning pipeline from data preparation and model training to prediction and deployment works successfully and can be used for demonstrations, learning purposes, or further development.