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--- |
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library_name: 'huggingface_hub' |
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tags: |
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- regression |
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- linear-regression |
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- custom-model |
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--- |
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# Linear Regression Model for EU Hospital Wait Times |
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## Model Description |
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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. |
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## Purpose |
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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. |
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## Training Data |
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The model was trained on the `EUHospitalWaitTime.csv` dataset. The features used for training include: |
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- `Year` |
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- `MthAndYrCode` |
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- `FourAndUnder_sum` (Sum of patients waiting 4 hours and under) |
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- `FiveToTwelve_sum` (Sum of patients waiting 5 to 12 hours) |
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- `OverTwelve_sum` (Sum of patients waiting over 12 hours) |
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The target variable is `Total_sum` (Total sum of patients waiting). |
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## Performance Metrics |
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After training, the model's performance was evaluated on a validation set. |
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- **Mean Squared Error (MSE)**: 0.3396 |
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- **R-squared (R²)**: 0.9513 |
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## Limitations |
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- This is a simple linear model, which may not capture complex non-linear relationships in the data. |
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- The log-transformation of the target variable means predictions need to be inverse-transformed (`np.expm1`) to get the actual scale of wait times. |
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- The dataset used might have specific characteristics or biases that could affect generalization to other datasets. |
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- The model is trained on aggregate sum data, not individual patient data. |
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