README.md

---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license: apache-2.0
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets:
- original
task_categories:
- regression
task_ids:
- dynamic-pricing
pretty_name: Dynamic Pricing Model
---

# 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

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

<details>
<summary> Click to expand </summary>

| Hyperparameter   | Value   |
|------------------|---------|
| alpha            | 1       |
| copy_X           | True    |
| fit_intercept    | False   |
| max_iter         | 1000    |
| positive         | False   |
| precompute       | False   |
| random_state     |         |
| selection        | cyclic  |
| tol              | 0.0001  |
| warm_start       | False   |

</details>

### Model Plot

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</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Lasso(alpha=1, fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>Lasso</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.Lasso.html">?<span>Documentation for Lasso</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>Lasso(alpha=1, fit_intercept=False)</pre></div> </div></div></div></div>

## Evaluation Results

The model achieved the following results on the test set:
- **Mean Absolute Error (MAE)**: 50.31928636001356
- **R² Score**: 0.8854065597299239

### Key Insights:
- Longer ride durations increase costs significantly, which may justify adding a surcharge for long-distance rides.
- Evening bookings reduce costs, potentially indicating lower demand during these hours.
- The model's accuracy is dependent on high-quality feature data.

Refer to the plots and tables for detailed performance insights.

### Model Coefficients

| Feature | Coefficient |
|---------|-------------|
| Number_of_Riders | -0.1398 |
| Number_of_Drivers | 0.4665 |
| Number_of_Past_Rides | -0.0033 |
| Average_Ratings | -0.0000 |
| Expected_Ride_Duration | 3.4973 |
| Location_Category_Suburban | 0.0000 |
| Location_Category_Urban | -0.0000 |
| Customer_Loyalty_Status_Regular | 0.0000 |
| Customer_Loyalty_Status_Silver | 0.0000 |
| Time_of_Booking_Evening | -2.4212 |
| Time_of_Booking_Morning | -0.0000 |
| Time_of_Booking_Night | 0.0000 |
| Vehicle_Type_Premium | 39.5754 |

### Regression Equation

Cost of Ride = *-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

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.

# 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}
}
```