Data Preparation

Data preparation, the crucial initial step in many machine learning pipelines, focuses on transforming raw data into a suitable format for model training and analysis. Current research emphasizes developing automated and scalable solutions, including toolkits for large language model applications and frameworks for unified data manipulation using LLMs, often incorporating techniques like data augmentation and imputation to address issues like missing values and class imbalance. These advancements aim to improve model accuracy, reproducibility, and efficiency across diverse applications, from medical diagnosis to recommendation systems and natural language processing.