Melbourne House Price Prediction System with Gradio UI

This project builds a machine learning model to predict house prices in Melbourne based on property features such as number of rooms, land size, location, and distance from the city center. The model analyzes historical housing data to learn patterns and generate accurate price estimates.

The system includes an interactive Gradio web interface that allows users to enter property details and instantly receive a predicted house price.

The project is developed using Python, with libraries such as Pandas and NumPy for data processing, Scikit-learn for machine learning, Matplotlib/Seaborn for data visualization, and Gradio for building the user-friendly UI.