Spaces:
Sleeping
title: Hotel Booking Cancellation Risk
emoji: 🏨
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
Hotel Booking Cancellation Risk Predictor
Model description
A HistGradientBoosting classifier trained on the Hotel Booking Demand dataset (Antonio et al., 2019). Given eight booking parameters, the model returns the probability that the booking will be cancelled.
Test AUC: 0.9067
Intended use
Educational demo for the course Introduction to Digital Content and Artificial Intelligence, Universitat de València (2025/2026). Not intended for production use.
Inputs
| Field | Type | Description |
|---|---|---|
| Hotel type | Categorical | City Hotel or Resort Hotel |
| Lead time | Integer (days) | Days between booking and arrival |
| Deposit type | Categorical | No Deposit / Non Refund / Refundable |
| Market segment | Categorical | Channel through which booking was made |
| Special requests | Integer (0-5) | Number of special requests |
| Previous cancellations | Integer | Past cancellations by this guest |
| ADR | Float (€) | Average Daily Rate |
| Total nights | Integer | Length of stay |
Output
Probability of cancellation, labelled as Low (< 30%), Medium (30–60%), or High (> 60%).
Known limitations
- Trained on European hotel data (Portugal) from 2015–2017. May not generalise to other markets.
- Missing values for features not provided in the form are imputed with training-set medians.
- No concept drift detection — model performance may degrade over time.
- Labels are in English; the target audience may need localisation.
Privacy & GDPR
This demo accepts no personally identifiable information (PII). No data entered in the form is stored or logged. For a production deployment handling guest data, a full GDPR impact assessment would be required and EU-resident cloud infrastructure should be used (Azure West Europe or AWS eu-west regions).