Organizations around the world are leveraging predictive analytics and data science automation to gain agility and faster, more accurate decision-making. If want to move from traditional analytics to an ML-driven predictive process , there are some important steps to follow and must haves you need.
- Automated data collection with drag and drop data import and flexible database connectivity
- AI-Focused data preparation that includes data value cleansing, statistical data profiling, and data re-architecting
- AI-Powered Feature Engineering with feature hypothesis, query generation, and feature relevance validation
Download our free eBook to learn about critical steps in the process and the following 3 key features to accelerate the predictive analytics journey.