Project 04 · Machine learning
Machine Learning Model · Airbnb in London
Supervised classification of listings as relatively expensive or inexpensive within each accommodation type.
CONTEXT AND OBJECTIVE
Build a supervised reading to distinguish relatively expensive and inexpensive listings, comparing prices within each accommodation type.
DATA AND DIAGNOSIS
- The curated Airbnb London dataset is used as the starting point.
- A relative label by accommodation type is defined to avoid directly comparing different segments.
- The model is trained and read with a focus on interpretability, territorial signals and operational variables.
DELIVERY AND LEARNING
The delivery connects exploratory analysis and modeling, showing how a score can rank listings by relative tendency toward high price without turning it into a direct price prediction.