Sydney Property Prices Dashboard



The Motivation

As the old adage goes, for pragmatists anyway, you learn by doing. This especially applies to learning programming. So I set off into the virtual world to find real world data that wasn't curated to make the coders life easier.

I first scoured Kaggle for data I could use to build an interesting project. My first instinct was to find data that was close to me in terms of proximity.

Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

A simple search of 'Sydney' into the search bar yielded a few results, but being the typical Sydney resident, data of property prices seemed like an obvious option.

The Result

  • A fully custom built interactive dashboard programmed from scratch using Dash Plotly, a Python framework for building web analytic applications.
  • The front end application is deployed on Heroku, a cloud platform that makes it easy to deploy and manage Flask applications.
  • The data was obtained from Kaggle, a dataset with over 100,000 entries scraped from
  • Data wrangling, Exploratory Data Analysis and Data Visualisations were performed using Python and documented in both static and executable (using Binder) Jupyter Notebook for reproducibility.
  • All code and project details are open-source and shared on GitHub.