Oil Market Forecasting PoC using AWS Services

This repository contains a Proof of Concept (PoC) implementation for AI-driven oil market forecasting using AWS services. The solution leverages various AWS services to collect, process, analyze, and visualize oil market data for accurate trend predictions.

Project Structure

├── src/
│   ├── data/           # Data ingestion and processing scripts
│   ├── models/         # Machine learning model implementations
│   ├── utils/          # Utility functions and helpers
│   └── visualization/  # Data visualization scripts
├── tests/              # Unit tests
├── docs/              # Documentation
├── requirements.txt   # Python dependencies
└── README.md         # This file

Prerequisites

• Python 3.8+
• AWS Account with appropriate permissions
• AWS CLI configured
• Required Python packages (specified in requirements.txt)

AWS Services Used

• AWS IoT Core - Data collection from sensors
• Amazon S3 - Data storage
• Amazon Kinesis - Real-time data processing
• AWS Glue - ETL operations
• Amazon SageMaker - Machine learning model development
• Amazon QuickSight - Data visualization
• Amazon CloudWatch - Monitoring and logging

Setup Instructions

1. Clone the repository:

git clone [repository-url]
cd oil-market-forecasting

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Configure AWS credentials:

aws configure

5. Set up environment variables:

export AWS_REGION=your-region
export BUCKET_NAME=your-bucket-name
export IOT_ENDPOINT=your-iot-endpoint

Implementation Steps

1. Infrastructure Setup

   • Run the infrastructure setup script:

   python src/utils/setup_infrastructure.py

2. Data Collection

   • Configure IoT devices using the provided scripts in src/data/
   • Set up Kinesis streams for real-time data

3. Data Processing

   • Deploy ETL jobs using AWS Glue
   • Process and transform data for analysis

4. Model Development

   • Train and deploy machine learning models using SageMaker
   • Implement forecasting algorithms

5. Visualization

   • Set up QuickSight dashboards
   • Configure real-time monitoring

Usage

1. Data Ingestion

python src/data/ingest_data.py --source [source-name] --destination [s3-path]

2. Model Training

python src/models/train_model.py --data-path [data-path] --model-output [model-path]

3. Forecasting

python src/models/forecast.py --model-path [model-path] --input-data [input-path]

4. Visualization

python src/visualization/create_dashboard.py --data-source [source-path]

Monitoring and Maintenance

• Monitor the application using CloudWatch
• Check logs and metrics in the AWS Console
• Set up alerts for anomalies

Testing

Run the test suite:

pytest tests/

Documentation

Detailed documentation is available in the docs/ directory:

• Architecture Overview
• API Documentation
• Troubleshooting Guide
• Best Practices

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support and questions, please create an issue in the repository or contact the maintainers.