Predict. Improve. Play.

Socculator is an advanced AI-powered football player evaluation and goal prediction platform that leverages machine learning models to analyze player performance and predict goal-scoring potential in football matches.

• AI-Powered Predictions: Multiple machine learning models (XGBoost, SVR, Linear Regression, KNN) for accurate goal predictions
• Player Evaluation: Comprehensive player statistics and performance analysis
• Interactive Web Interface: Modern, responsive UI built with Next.js and Tailwind CSS
• User Authentication: Secure login/signup system with JWT tokens
• Real-time Analysis: SHAP explanations for model predictions
• Team Management: Support for 200+ international football teams
• Performance Reports: Detailed player performance reports with visualizations

• Framework: Next.js 14 with TypeScript
• Styling: Tailwind CSS with custom components
• UI Components: NextUI, Framer Motion for animations
• State Management: React hooks and cookies for authentication
• Charts: Chart.js and Recharts for data visualization

• Runtime: Node.js with Express.js
• Database: MongoDB with Mongoose ODM
• Authentication: JWT-based authentication with bcrypt password hashing
• ML Integration: Python scripts for machine learning predictions
• API: RESTful API endpoints for predictions and user management

• Models: XGBoost, Support Vector Regression, Linear Regression, K-Nearest Neighbors
• Data Processing: Pandas for data manipulation
• Feature Engineering: Label encoding for categorical variables
• Explainability: SHAP values for model interpretability
• Prediction Pipeline: Automated model selection and prediction generation

Socculator/
├── client/                    # Next.js frontend application
│   ├── src/
│   │   ├── app/              # App router pages
│   │   │   ├── (tabs)/       # Tab-based navigation
│   │   │   ├── form/         # Player prediction form
│   │   │   ├── login/        # Authentication pages
│   │   │   └── signup/
│   │   ├── components/       # Reusable UI components
│   │   │   ├── ui/           # Custom UI components
│   │   │   └── ...
│   │   ├── constants/        # Application constants
│   │   └── utils/           # Utility functions
│   ├── public/              # Static assets
│   └── package.json
├── server/                   # Node.js backend application
│   ├── routes/              # API route handlers
│   ├── middleware/          # Custom middleware
│   ├── modles/              # Database models
│   ├── Python/              # Machine learning scripts
│   ├── *.joblib             # Trained ML models
│   └── goalscorers_with_winner.csv  # Training data
└── README.md

• Node.js (v18 or higher)
• Python (v3.8 or higher)
• MongoDB
• npm or yarn

cd client
npm install
npm run dev

cd server
npm install
npm start

pip install pandas scikit-learn joblib shap numpy

• Input: Player name, team, opponent team, match outcome
• Output: Predicted goals with detailed explanations
• Models: Multiple ML algorithms for robust predictions
• Explainability: SHAP values provide insights into prediction factors

• Security: JWT tokens with bcrypt password hashing
• User Management: Registration, login, and profile management
• Protected Routes: Authentication-required pages and API endpoints

• Charts: Interactive charts for player performance
• Reports: Comprehensive player statistics and trends
• UI/UX: Modern, responsive design with smooth animations

• Data Processing: CSV data with player statistics and match outcomes
• Feature Engineering: Team and player encoding for ML models
• Model Training: Multiple algorithms for comparison and selection
• Prediction API: RESTful endpoints for real-time predictions

• POST /auth/ - User registration
• POST /auth/login - User login
• POST /auth/fetchuser - Get user details

• POST /auth/submit - Submit prediction request
• POST /auth/getreport - Get player performance report

1. XGBoost: Gradient boosting for complex pattern recognition
2. Support Vector Regression (SVR): Non-linear regression with RBF kernel
3. Linear Regression: Simple linear relationship modeling
4. K-Nearest Neighbors (KNN): Instance-based learning

• Player Encoding: Numerical representation of player names
• Team Encoding: Numerical representation of team names
• Match Context: Home/away team and match outcome
• Performance Metrics: Historical goal-scoring data

• BackgroundBeams: Animated background effects
• MovingBorder: Interactive border animations
• AnimatedTooltip: Hover tooltips with animations
• NavbarMenu: Responsive navigation menu
• WavyBackground: Dynamic wave animations

• Home: Landing page with hero section
• Form: Player prediction interface
• Login/Signup: Authentication pages
• Player Details: Performance analysis and reports

1. Clone the repository
2. Set up the database: Configure MongoDB connection
3. Install dependencies: Run npm install in both client and server directories
4. Start the development servers:
   • Frontend: npm run dev (runs on port 3000)
   • Backend: npm start (runs on port 3002)
5. Access the application: Open http://localhost:3000

• Password Hashing: bcrypt with salt rounds
• JWT Authentication: Secure token-based authentication
• Input Validation: Express-validator for request validation
• CORS Configuration: Cross-origin resource sharing setup
• Environment Variables: Secure configuration management

• Model Caching: Pre-trained models loaded once
• Efficient Data Processing: Pandas for fast data manipulation
• Client-side Routing: Next.js App Router for optimal performance
• Component Optimization: React best practices for rendering

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is licensed under the MIT License.

• Data Source: Football match data and player statistics
• ML Libraries: scikit-learn, XGBoost, SHAP
• Frontend: Next.js, React, Tailwind CSS
• Backend: Node.js, Express.js, MongoDB

Socculator 1.0 - Where AI meets Football ⚽🤖