Objective / Purpose

Develop an end-to-end machine learning platform for financial market data, enabling real-time and batch analytics for investment decision-making. The system focuses on efficient data ingestion, model training, deployment, and monitoring for financial analytics pipelines.

Tools & Technologies Used

• Programming & ML: Python, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch
• Data Pipelines & Orchestration: Airflow, Kafka, Spark, SQL
• Platform & Deployment: Kubernetes (K8s), Kubeflow, CI/CD pipelines
• Model Serving: Triton, torchserve, BentoML, ONNX
• Visualization & Reporting: Power BI, Matplotlib

Methodology / Architecture

• Data Ingestion: Streaming and batch financial market data pipelines for stocks and crypto using Kafka + Spark.
• Data Processing: Cleaning, transformation, and feature engineering in scalable Python pipelines.
• Model Training: LSTM and other ML models trained on real-time and historical data using Kubeflow + K8s.
• Model Deployment & Monitoring: Automated deployment via CI/CD pipelines; real-time model monitoring for latency and performance metrics.
• Integration: Outputs integrated with dashboards for investment insights and decision support.

Key Results

• Delivered low-latency analytics pipelines for both real-time and batch market data.
• Achieved accurate investment insights, integrated into dashboards for actionable decision-making.
• Built a scalable, end-to-end ML platform that mirrors production-grade financial systems.

1️⃣ Clone the Repository cd AI-Finance-Analytics

2️⃣ Setup Virtual Environment cd backend

python -m venv venv source venv/bin/activate # (Linux/Mac) venv\Scripts\activate # (Windows)

3️⃣ Install Dependencies pip install -r requirements.txt

4️⃣ Run Flask Server python app.py

Server runs at: http://127.0.0.1:5000/

5️⃣ View Frontend

Open http://127.0.0.1:5000/ in your browser.

🧪 Example Use Cases

• Upload your monthly financial data (CSV/Excel).
• View Revenue Growth, Retention, GMV, and Profit margins.
• Predict next quarter’s financial trends using ML models.
• Run SQL queries directly from the dashboard.

🔮 Future Enhancements

✅ Integration with Mixpanel/Amplitude for product analytics.

✅ Real-time streaming data using Kafka.

✅ Deploy with Docker + AWS RDS + EC2.