Melbourne Real Estate Market Analysis

I built the project end-to-end — from data cleaning and exploratory analysis to machine learning, AI-assisted insights, and an interactive Power BI dashboard.

The results:

• 13K+ properties analyzed

• 5 machine learning models compared

• 89.9% classification accuracy with Random Forest

• Location emerged as one of the strongest factors associated with property price

But the most interesting part wasn't the 89.9%.

It was seeing how strongly location, distance from the CBD, and property characteristics shape the market — and turning those findings into insights that can actually support better decisions.

In other words:

Location matters.

But data shows us just how much.

This project was a great opportunity to bring together Python, Power BI, Machine Learning, and AI-assisted analytics into one complete, business-focused workflow.

github : https://github.com/Mostaf...