Personalized Finance Management System

A comprehensive full-stack application that combines personal finance management with AI-powered recommendations and social features. The system helps users manage their budgets, track expenses, and receive personalized financial advice.

🌟 Features

💰 Budget Planning

• Interactive budget planner with pie chart visualization
• AI-powered budget recommendations based on user input
• Customizable expense categories and subcategories
• Real-time budget tracking and updates

🤖 AI/ML Capabilities

• Smart budget allocation suggestions
• Receipt processing and categorization
• Financial stress score analysis
• Personalized financial recommendations

👥 Social Features

• User authentication system
• Crowdfunding capabilities
• Goal tracking and sharing
• Streak maintenance for financial habits

🛠 Tech Stack

Frontend

• React with TypeScript
• Vite for build tooling
• TailwindCSS for styling
• Chart.js for data visualization
• Lucide React for icons

Backend

• Node.js with Express
• MongoDB database
• JWT authentication
• Email notification system
• Automated schedulers

AI/ML Service

• Python
• Machine learning models for budget analysis
• Natural Language Processing for user input processing
• Financial recommendation engine

📦 Installation

Prerequisites

• Node.js (v14 or higher)
• Python (v3.8 or higher)
• MongoDB

Setup Instructions

1. Clone the repository

   git clone https://github.com/udayvalera/Personalized_Finance_Management.git
   cd Personalized_Finance_Management

2. Frontend Setup

   cd frontend
   npm install

3. Backend Setup

   cd backend
   npm install

4. AI/ML Service Setup

   cd ai-ml
   pip install -r requirements.txt

Environment Variables

In each directory, set the values in .env.example, then rename the file to .env by removing .example.

🚀 Running the Application

The application can be started using any of the following scripts:

Frontend

cd frontend && npm run dev

Backend

cd backend && node app.js

Using Command Prompt (Windows)

cd ai-ml && python app.py

These scripts will:

1. Create necessary log directories
2. Install dependencies for all components
3. Start the backend server
4. Launch the AI/ML service
5. Start the frontend development server
6. Collect logs in the logs directory

📁 Project Structure

├── frontend/           # React TypeScript frontend
├── backend/            # Node.js Express backend
└── ai-ml/              # Python AI/ML service

🔧 Configuration

Frontend

• Environment variables in frontend/src/.env
• Vite configuration in frontend/vite.config.ts
• TypeScript configuration in frontend/tsconfig.json

Backend

• Database configuration in backend/config/database.js
• Environment variables in backend/.env
• Route configurations in backend/routes/

AI/ML Service

• Model configurations in ai-ml/config/settings.py
• Environment variables in ai-ml/.env

🤝 Contributing

1. Fork the repository
2. Create your 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.

🙏 Acknowledgments

• Chart.js for data visualization
• TailwindCSS for the UI framework
• MongoDB for database solutions
• Python community for AI/ML tools