You're faced with complex feature engineering tasks. How do you prioritize quick results for stakeholders?
In data science, feature engineering is a crucial step that can significantly impact the performance of machine learning models. It involves creating new features from raw data to improve model accuracy. However, when time is of the essence and stakeholders are awaiting results, it's vital to prioritize tasks efficiently. This article explores practical strategies for managing complex feature engineering tasks while ensuring quick delivery of results to stakeholders.
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Tejas Satish NavalkheData Scientist | MS Data Science (AI Specialisation) at Newcastle University | Machine Learning | Deep Learning | LLMs…
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Shobhit DiggikarActively Seeking Data Analyst position | Aspiring Data Scientist | Python | MySQL | Tableau | Statistics | Data Science…
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Kapil GagnejaSr Technical Manager at Expedia | AI & ML evangelist | Ex-Amazon