In this blog, we'll walk you through our transition to supervised classification, share how we tackled some intriguing challenges, and reveal how we expanded our model to handle multiple ecosystems. Whether you're a data science enthusiast or a curious ML developer, there's something here for everyone!

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.