MACHINE LEARNING · PROJECT 09
Project 04 · Machine learning
Machine Learning Model · Airbnb in London
Classification model with logistic regression to identify relatively expensive or inexpensive listings within each accommodation type.
CONTEXT AND OBJECTIVE
Classify listings according to their relative tendency toward higher or lower prices within each accommodation type, without treating price as a direct prediction.
DATA AND DIAGNOSIS
- The curated Airbnb London dataset is used as the starting point.
- The target variable is defined from relative price extremes within each accommodation type, so listings are compared within equivalent segments.
- A logistic regression is trained with availability, reviews, minimum stay, host size and neighbourhood variables, together with evaluation and calibration metrics.
DELIVERY AND LEARNING
The final output turns the model into a relative-tendency score that ranks listings and separates low-, standard- and high-price zones without presenting it as a direct price prediction.