LONDON AIRBNB LISTINGS ANALYTICS

This project implements a complete Knowledge Discovery in Databases (KDD) pipeline to analyze 28,480 London Airbnb listings. Built as part of the ICT 333 1.5 module at the University of Sri Jayewardenepura, the project demonstrates a rigorous, reproducible approach to data engineering.

The pipeline includes custom data extraction scripts that pull raw listing data, followed by comprehensive data cleansing routines that handle missing values, outliers, and inconsistent records. Every step is documented and scripted to ensure full reproducibility.

The analysis phase applies statistical methods to uncover patterns in pricing, availability, and listing characteristics across London's neighborhoods. Results are presented through clear visualizations and summary statistics that support data-driven conclusions.

TECH STACK

KEY FEATURES

• Custom data extraction scripts for reproducibility
• Comprehensive data cleansing and preprocessing
• Statistical analysis of 28,480 listings
• Visualizations of pricing and availability patterns
• Documented KDD pipeline methodology