| --- |
| 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. |
|
|
