DataForge OpenAI Hub is dedicated to building practical, real-world solutions leveraging the power of machine learning, MLOps, and data analytics pipelines. Explore our two main projects below!

This repository implements an end-to-end ETL pipeline for analyzing Steam sales data. It retrieves, processes, and stores gaming metadata and sales data, offering insightful trends and performance analysis.

• ETL Pipeline: Automates the retrieval, validation, and processing of data from SteamSpy and Steam APIs.
• Data Storage: Stored in a MySQL database hosted on Aiven Cloud.
• Visualization: Interactive Tableau dashboards provide insights into gaming trends.
• Automation: Fully automated using CLI-based orchestration.
• Open-source contribution: Deployed and maintained a PyPI package/library (open-source contribution).

📂 Technologies Used: Python, MySQL, Tableau, Steam API, Typer CLI, ETL

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This repository presents a comprehensive machine learning project that tackles credit card fraud detection using MLOps best practices. Highlights include:

• Ensemble Models: Combines multiple algorithms for better accuracy in detecting fraudulent transactions.
• Data & Model Versioning: Managed with DVC to ensure consistent and reproducible results.
• CI/CD Pipelines: Implemented with GitHub Actions to automate the workflow.
• Deployment: Model deployment to production with robust monitoring.

📂 Technologies Used: Python, Jupyter, Scikit-learn, DVC, GitHub Actions, Docker

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Clone the respective repository and follow the setup instructions provided in each project.

# Clone the Data Pipeline project
git clone https://github.com/DataForgeOpenAIHub/Steam-Sales-Analysis.git

# Clone the ML project
git clone https://github.com/DataForgeOpenAIHub/mlops-credit-card-fraud-detection-end-to-end.git