This story was originally published on HackerNoon at: https://hackernoon.com/why-accuracy-fails-for-uplift-models-and-what-to-use-instead.
When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.
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When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.

This story was originally published on HackerNoon at: https://hackernoon.com/turning-your-data-swamp-into-gold-a-developers-guide-to-nlp-on-legacy-logs.
A practical NLP pipeline for cleaning legacy maintenance logs using normalization, TF-IDF, and cosine similarity to detect fraud and improve data quality.
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The NLP Cleaning Pipeline is a tool to clean, vectorize, and analyze unstructured "free-text" logs. It uses Python 3.9+ and Scikit-Learn for vectorization and similarity metrics. The pipeline uses Unicode normalization, the Thesaurus, and case folding to remove noise.

This story was originally published on HackerNoon at: https://hackernoon.com/data-monetization-strategies-in-government-digital-platforms.
How governments monetize digital data to drive innovation, trust, transparency and economic value.
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Government data is not merely a by-product of governance, it's a strategic asset, writes Frida Ghitis. Ghitis: Government cannot be a data broker, but it should be the custodian of the value of the information it possesses.