Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,35 @@
+---
+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.