Built a real-time Dublin rental analytics platform using scraped data from Daft.ie listings. Leveraged Apache Airflow for ETL automation, conducted feature-rich exploratory data analysis, and built predictive models to estimate rents and rank listings based on user preferences. The final solution includes an interactive frontend hosted via GitHub Pages.
Dublin’s rental market lacks transparency and personalized tools for renters to estimate fair pricing and compare neighborhoods. This project aims to bridge that gap by offering renters intelligent insights and predictions to support informed decision-making.
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Fig 1 : Distribution of monthly rent prices across Dublin listings, showing a skewed trend toward mid-to-high ranges.
Fig 2 : Correlation heatmap from EDA highlighting relationships among numeric features like price, bedrooms, and BER ratings
Fig 3 : Bar plot showing average rent per Dublin subcode, revealing spatial disparities in pricing