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Propensity Modeling | The Importance of Feature Engineering for AI
Organizations seeking to be data-driven are increasingly turning to more sophisticated data analysis, including machine learning & predictive modeling. When executed correctly, machine learning can generate predictive insights and actionable intelligence that can boost fundraising outcomes.

Unfortunately, effective machine learning models can be difficult to create, and an essential component that is often overlooked in the process is feature engineering. Feature engineering is the process of creating new data inputs, often by modifying or combining existing elements, to produce new features that improve the predictive power of machine learning algorithms. In this session, Windfall CEO Arup Banerjee explores the role of feature engineering in building propensity models and how data-driven organizations employ it to improve the effectiveness of their fundraising efforts.

Register today for this LIVE, exclusive webinar for Apra attendees.

Arup Banerjee, CEO, Windfall
Chris Ferraioli, Senior Data Scientist, Windfall

*1PT CFRE Credit

Sep 30, 2020 01:00 PM in Central Time (US and Canada)

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