Data preparation

ML models are only as good as the data that is used to train them. Ensure that suitable training data is available and is optimized for learning and generalization. Data preparation includes data preprocessing and feature engineering.

A key aspect to understanding data is to identify patterns. These patterns are often not evident with data in tables. Exploratory data analysis (EDA) with visualization tools can help in quickly gaining a deeper understanding of data. Prepare data using data wrangler tools for interactive data analysis and model building. Employ no-code/low-code, automation, and visual capabilities to improve the productivity and reduce the cost for interactive analysis.