Effective data preprocessing is key to reducing outliers and unlocking the true potential of open-source sustainability insights.

In this blog we’ll walk you through cleaning and scaling the collected data in order to address issues like missing or inconsistent information, transform data into a suitable format, and create composite metrics to better assess the sustainability of open-source packages.

Machine Learning thrives on data & feature engineering, shaping models for accurate predictions.

In our latest blog, we explore how Package Sustainability Scanner (PSS) we used data and Feature engineering to enhance ML results

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In today’s fast-paced digital world, managing software dependencies is crucial to avoid security risks and technical debt. The Package Sustainability Scanner (PSS), powered by machine learning and data modeling, evaluates the long-term viability of open-source packages from the ecosystems such as PyPI and npm by analyzing maintainability, engagement and community support.