Taxi Fare Prediction using Django

A modern web application that predicts taxi fares using machine learning models deployed with Django. The application features a beautiful, responsive UI and uses Ridge Regression and Decision Tree algorithms to provide accurate fare estimates.

Features

• Multiple ML Models: Ridge Regression and Decision Tree for fare prediction
• Modern UI: Beautiful, responsive interface with animations and gradients
• Real-time Predictions: Instant fare calculations based on trip parameters
• Mobile Responsive: Works seamlessly on all devices
• Interactive Design: Smooth animations and user-friendly experience

Tech Stack

• Backend: Django 4.x, Python 3.13
• Frontend: HTML5, CSS3, JavaScript
• Machine Learning: scikit-learn
• Database: SQLite (default)
• Styling: Custom CSS with modern animations and gradients

Prerequisites

Before running this project, make sure you have:

• Python 3.8 or higher
• pip (Python package installer)
• Git

Installation & Setup

1. Clone the repository

   git clone https://github.com/malakasaber/Model-Deployment-using-Django.git
   cd Model-Deployment-using-Django

2. Create and activate virtual environment

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install django scikit-learn pandas numpy

4. Run database migrations

   python manage.py migrate

5. Start the development server

   python manage.py runserver

6. Open your browser Navigate to http://127.0.0.1:8000/ to see the application.

Usage

1. Fill in the form with the following trip details:

   • Pickup and dropoff coordinates (longitude/latitude)
   • Passenger count
   • Distances to key locations (JFK, EWR, LGA airports, Statue of Liberty, NYC)
   • Trip distance and bearing

2. Click "Predict Fare" to get instant predictions

3. View results in the beautiful modal popup showing predictions from both models

Project Structure

DjangoTask/
├── deploymentproject/          # Django project settings
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   ├── wsgi.py
│   └── asgi.py
├── myapp/                      # Main Django app
│   ├── models/                 # Pre-trained ML models
│   │   ├── transformer.pkl
│   │   ├── transformer2.pkl
│   │   ├── XGB.pkl
│   │   ├── Linear.pkl
│   │   ├── KNN.pkl
│   │   ├── ridgeregression.pkl
│   │   └── decisiontree.pkl
│   ├── templates/myapp/        # HTML templates
│   │   └── index.html
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── ml_pipeline.py          # ML model loading and prediction logic
│   ├── models.py
│   ├── model2.py
│   ├── urls.py
│   ├── views.py
│   └── tests.py
├── db.sqlite3                  # SQLite database
├── manage.py                   # Django management script
└── README.md

Machine Learning Models

XGBoost

• Purpose: Gradient boosting framework optimized for speed and performance
• Strengths: High accuracy, handles missing data, effective on structured data
• Use Case: Predicts fares with fine-grained performance tuning and scalability

Linear Regression

• Purpose: Models linear relationship between input variables and output
• Strengths: Fast, interpretable, works well with linearly correlated data
• Use Case: Predicts fares based on distance when the relationship is linear

K-Nearest Neighbors (KNN)

• Purpose: Instance-based learning model for classification and regression
• Strengths: Simple, non-parametric, adapts well to local data structure
• Use Case: Estimates fares by comparing with similar past trips

Ridge Regression

• Purpose: Linear regression with L2 regularization
• Strengths: Good for handling multicollinearity, stable predictions
• Use Case: Baseline model for fare prediction

Decision Tree

• Purpose: Non-linear tree-based regression model
• Strengths: Captures complex patterns, easy to interpret
• Use Case: Handles non-linear relationships in fare calculation

📊 Input Features

The model uses the following 20 features for prediction:

Feature	Description
pickup_longitude	Pickup location longitude
pickup_latitude	Pickup location latitude
dropoff_longitude	Dropoff location longitude
dropoff_latitude	Dropoff location latitude
passenger_count	Number of passengers
jfk_dist	Distance to JFK Airport
ewr_dist	Distance to Newark Airport
lga_dist	Distance to LaGuardia Airport
sol_dist	Distance to Statue of Liberty
nyc_dist	Distance to NYC center
distance	Trip distance
bearing	Direction of travel

UI Features

• Modern Design: Gradient backgrounds and glassmorphism effects
• Responsive Layout: Grid-based form layout that adapts to screen size
• Interactive Elements: Hover effects, smooth transitions, and animations
• Visual Feedback: Loading spinners, input validation, and success modals
• Accessibility: Proper contrast ratios and semantic HTML

Development

Adding New Models

1. Train your model and save it as a .pkl file
2. Place it in the myapp/models/ directory
3. Update ml_pipeline.py to load and use your model
4. Modify the view and template to display the new predictions

Customizing the UI

• Edit myapp/templates/myapp/index.html for structure and styling
• Modify CSS classes for different visual themes
• Add new animations or interactive elements as needed

API Endpoints

• GET / - Main page with prediction form
• POST / - Submit prediction request and get results

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

Authors

Malak Ahmed Saber

• GitHub: @malakasaber

Marawan Abbas

• Github: @MarwanAbbas205

Acknowledgments

• Built during Cellula Technologies Internship
• Inspired by real-world taxi fare prediction systems
• UI design influenced by modern web design trends

Support

If you have any questions or run into issues, please:

1. Check the existing issues on GitHub
2. Create a new issue with detailed information
3. Contact the maintainer

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