Property Prices in London & Regional Affordability

This project explored housing affordability across the UK, with a focus on predicting London house prices using data from Kaggle and the Office for National Statistics (ONS).

Key highlights:

Cleaned and explored datasets using Python and Excel

Engineered geospatial and categorical features (e.g., transport proximity using GeoPandas)

Trained a Random Forest Regressor model with an R² of 0.73 for both the train split and test spilt with an RMSE ≈ £195K which was a about 5%.

Built interactive Tableau dashboards to communicate regional affordability insights

The project aims to support planners, policymakers, and investors with data-driven insights into housing market disparities.

Please find links to my GitHub and Tableau Public, for further information