Improve AI/ML model performance and accuracy for competitive advantage
Retailers worldwide are leveraging AI/ML technologies to derive insights from data and make predictions to solve core business challenges and respond to emerging scenarios in real time. Every intelligent system used by retailers has at its core machine learning, deep learning, image/video processing algorithms or other statistical models. However, the success of these models depends on how well the raw data sources are processed to extract key features. Feature engineering in data science is the process of transforming raw data into key features that can be used as input variables to derive best results from algorithms. Businesses that engineer raw data and extract maximum insights from these models will emerge as winners.
This paper explains how to isolate key information from data noise, connect the dots, and highlight patterns to maximize the outcomes from AI implementations with feature engineering, and get a clear edge over competition.