NYC FareFinder Web App 🚖💵

NYC FareFinder is a web application designed to predict taxi fares in New York City using machine learning. Built with a modern tech stack, the app leverages an Extra Trees Regressor model (R² score of 0.94) to provide accurate fare predictions, helping users plan their trips efficiently.

Screenshots

Below are some screenshots showcasing the NYC FareFinder interface:

Fare Prediction Form	Prediction Result

Features

• Accurate Fare Prediction: Predicts taxi fares using an Extra Trees Regressor (R² score: 0.94) based on factors like passenger count, traffic, weather conditions, pickup time, and distance.
• Responsive Design: Fully responsive layout using Tailwind CSS, with a glowing form border for user inputs.

Tech Stack

• Frontend: Vite, React (TypeScript), Tailwind CSS
• Backend: Django (Python)

Prerequisites

Before setting up the project, ensure you have the following installed:

• Node.js: v22.14.0
• npm: 11.2.0
• Python: 3.11.9

Project Structure

The project is divided into three main directories:

• fare-prediction-frontend/: Contains the React frontend.
• fare_prediction/: Contains the Django backend and ML models.
• notebooks/: Contains Jupyter notebooks for EDA, Preprocessing and Training of various Regressor models.

How to Use the Notebooks

1. Option 1: Train the Model Yourself

   • Download the dataset from this link.
   • Navigate to the notebooks/ directory and run the Jupyter notebooks in order.

2. Option 2: Use the Pre-Trained Model

   • Download our existing trained Extra Trees Regressor model from this link.
   • Place the Extra_Trees.pkl file in the fare_prediction/models/ directory.

Setup Instructions

Step 1: Clone the Repository

Clone the project repository to your local machine:

git clone https://github.com/mohababsa/nyc-farefinder.git
cd nyc-farefinder

Step 2: Set Up the Virtual Environment

Create and activate the virtual environment:

python -m venv fare_prediction_venv
.\fare_prediction_venv\Scripts\activate  # Windows
# For macOS/Linux: source fare_prediction_venv/bin/activate

Install python dependencies:

pip install -r requirements.txt

Step 3: Set Up the Backend (Python/Django)

Navigate to the backend directory and run the Django development server to ensure the backend is working:

cd fare_prediction
python manage.py runserver

The backend should be accessible at http://localhost:8000.

Step 4: Set Up the Frontend (React)

Navigate to the Frontend Directory:

cd ../fare-prediction-frontend

Ensure you have Node.js v22.14.0 and npm 11.2.0. Install the required packages:

npm install

The app uses icons from @heroicons/react/24/outline. Install it:

npm install @heroicons/react

Verify Tailwind CSS and Vite Configuration. Start the React development server:

npm run dev

The frontend should be accessible at http://localhost:5173.

Contributing

Contributions are welcome! Please fork the repository, create a new branch, and submit a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Built as part of an internship project at CellulaTechnologies.