This project is a Taxi Fare Prediction system that utilizes machine learning techniques to estimate taxi fares based on various factors such as distance, time, location, and more. It aims to provide accurate fare estimates to passengers and help taxi service providers optimize pricing.
- Predict taxi fares based on input parameters.
- User-friendly interface for fare estimation.
- Integration with mapping services for route calculation.
You can try out the Taxi Fare Prediction system by visiting our Demo Page.
[Include screenshots or GIFs of the demo here]
To set up this project locally, follow these steps:
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Clone the repository
git clone https://github.com/yourusername/taxi-fare-prediction.git cd taxi-fare-prediction -
Create a virtual environment (recommended) and install dependencies:
python -m venv venv source venv/bin/activate # On Windows, use: venv\Scripts\activate pip install -r requirements.txt -
Run the application: python app.py
The application should now be running locally at http://localhost:5000.
Enter the pickup and dropoff locations along with other parameters. Click the "Estimate Fare" button to get the predicted taxi fare.
The dataset used for this project can be found in the data directory. It includes historical taxi trip data used for training the machine learning model.
The machine learning model used for fare prediction is implemented in the train_model.ipynb Jupyter Notebook. You can explore this notebook for details on data preprocessing, feature engineering, model selection, and training.
Contributions are welcome! If you'd like to improve this project or add new features, please open an issue or submit a pull request. For major changes, please discuss them in advance.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions or collaborations, contact Swarnava Gayen.