Pushing model and README files to the repo!

README.md

@@ -19,7 +19,7 @@ pretty_name: Dynamic Pricing Model
 
 # Model description
 
-[More Information Needed]
+This is a regression model trained on the Dynamic Pricing Dataset. It was optimized using grid search with multiple hyperparameters.
 
 ## Intended uses & limitations
 
@@ -170,11 +170,7 @@ If you use this model, please cite it as follows:
 }
 ```
 
-# # Model description
-
-This is a regression model trained on the Dynamic Pricing Dataset. It was optimized using grid search with multiple hyperparameters.
-
-# ## Intended uses & limitations
+# Intended Uses & Limitations
 
 This regression model is designed to predict the cost of rides based on various features such as expected ride duration, number of drivers, and time of booking.
 
@@ -187,15 +183,32 @@ This regression model is designed to predict the cost of rides based on various
 - **Dataset Specificity**: Performance may degrade if applied to datasets with significantly different distributions.
 - **Outlier Sensitivity**: Predictions can be affected by extreme values in the dataset.
 
-# ## Training Procedure
+# Training Procedure
 
 The model was trained using grid search to optimize hyperparameters. Cross-validation (5-fold) was performed to ensure robust evaluation. The best model was selected based on the lowest Mean Absolute Error (MAE) on the validation set.
 
+# 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
 
 ## Model Coefficients
 
-## Model Coefficients| Feature | Coefficient |
+### Model Coefficients:
+
+| Feature | Coefficient |
 |---------|-------------|
 | Number_of_Riders | -0.1398 |
 | Number_of_Drivers | 0.4665 |
@@ -217,13 +230,15 @@ The model was trained using grid search to optimize hyperparameters. Cross-valid
 
 ## Actual vs Predicted
 
-## Actual vs PredictedThe following plot shows the relationship between the actual and predicted values. The closer the points are to the diagonal line, the better the predictions. The dashed line represents the ideal case where predictions perfectly match the actual values.
+### Actual vs Predicted Plot
+
+The following plot shows the relationship between the actual and predicted values. The closer the points are to the diagonal line, the better the predictions. The dashed line represents the ideal case where predictions perfectly match the actual values.
 
 ![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.
 
-# Evaluation results
+# Evaluation Results
 
 The model achieved the following results on the test set:
 - **Mean Absolute Error (MAE)**: 50.32