Pushing model and README files to the repo!

README.md

@@ -23,11 +23,20 @@ This is a regression model trained on the Dynamic Pricing Dataset. It was optimi
 
 ## Intended uses & limitations
 
-[More Information Needed]
+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.
+
+**Intended Uses**:
+- **Dynamic Pricing Analysis**: Helps optimize pricing strategies for ride-hailing platforms.
+- **Demand Forecasting**: Supports business decisions by estimating cost trends based on ride-specific parameters.
+
+**Limitations**:
+- **Feature Dependence**: The model's accuracy is highly dependent on the input features provided.
+- **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
 
-[More Information Needed]
+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
 
@@ -170,23 +179,6 @@ If you use this model, please cite it as follows:
 }
 ```
 
-# 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.
-
-**Intended Uses**:
-- **Dynamic Pricing Analysis**: Helps optimize pricing strategies for ride-hailing platforms.
-- **Demand Forecasting**: Supports business decisions by estimating cost trends based on ride-specific parameters.
-
-**Limitations**:
-- **Feature Dependence**: The model's accuracy is highly dependent on the input features provided.
-- **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
-
-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:
@@ -202,7 +194,13 @@ The model was trained using grid search to optimize hyperparameters. Cross-valid
 - tol: 0.0001
 - warm_start: False
 
-# Evaluation
+# 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
 
@@ -237,11 +235,3 @@ 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.
-
-# 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.