Pushing model and README files to the repo!

README.md

@@ -136,31 +136,56 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 
 # How to Get Started with the Model
 
-[More Information Needed]
+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 is written by following authors:
-
-[More Information Needed]
+This model card was written by **Pranav Sharma**.
 
 # Model Card Contact
 
-You can contact the model card authors through following channels:
-[More Information Needed]
+For inquiries or feedback, you can contact the author via **[GitHub](https://github.com/PranavSharma)**.
 
 # Citation
 
-Below you can find information related to citation.
-
-**BibTeX:**
+If you use this model, please cite it as follows:
 ```
-[More Information Needed]
+@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}
+}
 ```
 
+# 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.
+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
 
@@ -205,4 +230,18 @@ The model was trained using grid search to optimize hyperparameters.
 
 ## Actual vs Predicted
 
+### 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
+
+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.