---
library_name: 'huggingface_hub'
tags:
- regression
- linear-regression
- custom-model
---
# Linear Regression Model for EU Hospital Wait Times

## Model Description
This is a custom-implemented Linear Regression model trained to predict European Union hospital wait times. The model was built from scratch using NumPy and trained with a Mini-Batch Gradient Descent optimization algorithm.

## Purpose
The primary purpose of this model is to provide a predictive tool for total hospital wait times based on various categories of wait times (e.g., 'FourAndUnder_sum', 'FiveToTwelve_sum', 'OverTwelve_sum'). The target variable, 'Total_sum', was log-transformed (`np.log1p`) during training to handle potential skewness and improve model performance.

## Training Data
The model was trained on the `EUHospitalWaitTime.csv` dataset. The features used for training include:
- `Year`
- `MthAndYrCode`
- `FourAndUnder_sum` (Sum of patients waiting 4 hours and under)
- `FiveToTwelve_sum` (Sum of patients waiting 5 to 12 hours)
- `OverTwelve_sum` (Sum of patients waiting over 12 hours)

The target variable is `Total_sum` (Total sum of patients waiting).

## Performance Metrics
After training, the model's performance was evaluated on a validation set.
- **Mean Squared Error (MSE)**: 0.3396
- **R-squared (R²)**: 0.9513

## Limitations
- This is a simple linear model, which may not capture complex non-linear relationships in the data.
- The log-transformation of the target variable means predictions need to be inverse-transformed (`np.expm1`) to get the actual scale of wait times.
- The dataset used might have specific characteristics or biases that could affect generalization to other datasets.
- The model is trained on aggregate sum data, not individual patient data.