---
library_name: xgboost
license: apache-2.0
tags:
- tabular-regression
- time-series
- synthetic-data
- xgboost
- demo
---

# synthetic-ae-demand-xgboost

**This model is trained entirely on synthetic data.** It is a demo/portfolio
project showing an end-to-end XGBoost forecasting pipeline, styled after an
A&E (emergency department) daily-demand forecasting problem. No real patient
or NHS data was used anywhere in this project.

## Model description

XGBoost regressor predicting a synthetic daily "attendance" count from
calendar features and rolling averages.

**Features:** day of week, day of year, weekend flag, synthetic-holiday flag,
7-day and 28-day rolling averages.

## Training data

Synthetic daily time series generated locally with weekly seasonality
(higher on weekends), annual seasonality (winter peak), a mild upward trend,
occasional synthetic "holiday" spikes, and Gaussian noise. Generation code is
included in this repo (`generate_synthetic_ae_data`) so the data is fully
reproducible and inspectable.

## Evaluation results

(on a chronological 80/20 train/test split of the synthetic data)

- **MAE**: 6.271
- **RMSE**: 8.163
- **R²**: 0.83
- **Train / test size**: 1200 / 300

## Intended use & limitations

Demo/portfolio use only. Because the training data is synthetic, this model
has no predictive value for real-world emergency department demand and
should not be used for anything operational or clinical.

## How to use

```python
import xgboost as xgb

model = xgb.XGBRegressor()
model.load_model("model.json")
preds = model.predict(X)  # X must have the same feature columns as training
```