Pushing model and README files to the repo!

README.md

@@ -141,68 +141,13 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 
 ## Evaluation Results
 
-[More Information Needed]
-
-# How to Get Started with the Model
-
-To use this model:
-1. **Install Dependencies**: Ensure `scikit-learn` and `pandas` are installed in your environment.
-2. **Load the Model**: Download the saved model file and load it using `joblib`:
-   ```python
-   from joblib import load
-   model = load('best_model.joblib')
-   ```
-3. **Prepare Input Features**: Create a DataFrame with the required input features in the same format as the training dataset.
-4. **Make Predictions**: Use the `predict` method to generate predictions:
-   ```python
-   predictions = model.predict(input_features)
-   ```
-
-# Model Card Authors
-
-This model card was written by **Pranav Sharma**.
-
-# Model Card Contact
-
-For inquiries or feedback, you can contact the author via **[GitHub](https://github.com/PranavSharma)**.
-
-# Citation
-
-If you use this model, please cite it as follows:
-```
-@model{pranav_sharma_dynamic_pricing_model_2025,
-  author       = {Pranav Sharma},
-  title        = {Dynamic Pricing Model},
-  year         = {2025},
-  version      = {1.0.0},
-  url          = {https://huggingface.co/PranavSharma/dynamic-pricing-model}
-}
-```
-
-# Hyperparameters
-
-### Hyperparameters:
-
-- alpha: 1
-- copy_X: True
-- fit_intercept: False
-- max_iter: 1000
-- positive: False
-- precompute: False
-- random_state: None
-- selection: cyclic
-- tol: 0.0001
-- warm_start: False
-
-# Evaluation Results
-
 The model achieved the following results on the test set:
 - **Mean Absolute Error (MAE)**: 50.32
 - **R² Score**: 0.89
 
 Refer to the plots and tables for detailed performance insights.
 
-## Model Coefficients
+### Model Coefficients
 
 ### Model Coefficients:
 
@@ -222,11 +167,11 @@ Refer to the plots and tables for detailed performance insights.
 | Time_of_Booking_Night | 0.0000 |
 | Vehicle_Type_Premium | 39.5754 |
 
-## Regression Equation
+### Regression Equation
 
 -0.1398 * Number_of_Riders + 0.4665 * Number_of_Drivers + -0.0033 * Number_of_Past_Rides + 3.4973 * Expected_Ride_Duration + -2.4212 * Time_of_Booking_Evening + 39.5754 * Vehicle_Type_Premium
 
-## Actual vs Predicted
+### Actual vs Predicted
 
 ### Actual vs Predicted Plot
 
@@ -235,3 +180,39 @@ The following plot shows the relationship between the actual and predicted value
 ![Actual vs Predicted Plot](actual_vs_predicted.png)
 
 The scatter plot above shows the predicted values against the actual values. The dashed line represents the ideal predictions where the predicted values are equal to the actual values.
+
+# How to Get Started with the Model
+
+To use this model:
+1. **Install Dependencies**: Ensure `scikit-learn` and `pandas` are installed in your environment.
+2. **Load the Model**: Download the saved model file and load it using `joblib`:
+   ```python
+   from joblib import load
+   model = load('best_model.joblib')
+   ```
+3. **Prepare Input Features**: Create a DataFrame with the required input features in the same format as the training dataset.
+4. **Make Predictions**: Use the `predict` method to generate predictions:
+   ```python
+   predictions = model.predict(input_features)
+   ```
+
+# Model Card Authors
+
+This model card was written by **Pranav Sharma**.
+
+# Model Card Contact
+
+For inquiries or feedback, you can contact the author via **[GitHub](https://github.com/PranavSharma)**.
+
+# Citation
+
+If you use this model, please cite it as follows:
+```
+@model{pranav_sharma_dynamic_pricing_model_2025,
+  author       = {Pranav Sharma},
+  title        = {Dynamic Pricing Model},
+  year         = {2025},
+  version      = {1.0.0},
+  url          = {https://huggingface.co/PranavSharma/dynamic-pricing-model}
+}
+```