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On-Demand Webinar

Data Preparation Essentials for Automated Machine Learning

In order to run successful machine learning projects, and create highly-accurate predictive models for your business, you need effective data preparation. Although machine learning automation provides safeguards to prevent common mistakes, you’ll still want to correctly prepare, shape and format your data to generate optimal models.


In this on-demand webinar, Jen Underwood, Founder of Impact Analytix reviews how to organize data in a machine learning-friendly format that accurately reflects the business process and outcomes. She shares basic guidelines, practical tips and additional resources to help get you started mastering the essence of predictive model data preparation.

Discover the secrets to model-building success related to:

  • Data collection and granularity
  • Data formats and structure
  • Analytical feature engineering
  • Dealing with data quality issues


  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.