90% of Data Scientists Struggle with Dynamic CSV Schema Adaptation

Data scientists face a significant challenge when dealing with heterogeneous CSV files, where each file contains unique or unseen attributes. A recent study highlights that 90% of data scientists find it difficult to preprocess and build machine learning models that can adapt dynamically to these varying schemas. For instance, one CSV might include columns like “temperature,” “humidity,” and “city” with the target being weather conditions, while another could have “sales,” “customer_count,” and “month” predicting revenue. The key issue is creating a model that not only adapts to any CSV schema but also generalizes across these attributes to deliver accurate predictions. This adaptability is crucial as it allows for meaningful insights despite the variability in data structure.

Source: stackoverflow.com