A robust, AI-powered currency exchange forecasting application that supports live rate tracking, conversion, and predictive analytics across 20+ countries. This project uses machine learning models to forecast exchange rate trends and provides real-time conversions using integrated APIs.
🔧 Features 🔁 Live Currency Conversion: Real-time currency conversion across 20+ global currencies using a public/exchange rate API (e.g., ExchangeRate-API, Forex Python, or similar).
📈 Forecasting Engine: Time-series forecasting models (ARIMA, Prophet, or LSTM) to predict exchange rate movements.
🌍 Country Support: Supports major currencies like USD, EUR, GBP, INR, JPY, AUD, CAD, and more.
📊 Visualization Dashboard: Matplotlib/Plotly-based charts showing historical trends, predicted rates, and comparative analysis.
🧠 AI/ML Models: Uses ML pipelines for trend analysis and forecasting based on historical currency data.
🧪 Testing & Error Handling: Built-in error detection for API failures, rate-lagging, and data inconsistencies.
🚀 Modular & Scalable Design: Easily extensible for additional currencies, model improvements, or integration into fintech apps.
🧠 Tech Stack Languages: Python
Libraries: pandas, numpy, scikit-learn, matplotlib, plotly, statsmodels, fbprophet, requests, flask
APIs: Live currency exchange APIs
Deployment: Flask (local), optionally Docker-ready
git clone https://github.com/your-username/currency_forecasting.git cd currency_forecasting pip install -r requirements.txt python app/routes.py 📌 Future Enhancements Add currency arbitrage detection
Integrate with payment APIs for direct transfers
Multi-lingual support for global users
📌 summary
Developed a full-stack AI-based currency exchange forecasting system supporting live rate tracking and predictive modeling for 20+ countries.
Integrated real-time exchange rate APIs to enable accurate currency conversions and historical data retrieval.
Built time-series forecasting models (ARIMA, Prophet, LSTM) to predict currency fluctuations, achieving high accuracy over rolling test windows.
Created an interactive dashboard using Plotly and Matplotlib for visualizing trends, predicted values, and cross-currency comparisons.
Designed a modular Flask backend to handle live inference, conversion requests, and scalable deployment.