The Complete Guide For Data Acquisition

Data scientists are constantly challenged with improving their ML models.

But when a new algorithm won’t improve your AUC there’s only one place to look: DATA.

Generating, testing, and integrating new features from various internal and/or external sources is time-consuming, difficult, and more “artistic.” But it could lead to a major discovery and move the needle much more.

This whitepaper breaks down:

  • Six easy-to-follow steps for data acquisition
  • Complete checklist for data provider due diligence
  • Data provider tests to uplift your model’s accuracy

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