Mine was building a forecasting model for a retail dataset. I went in thinking the model would be the hard part, but I ended up spending 80% of my time cleaning data, engineering features, and figuring out why my assumptions were wrong.

Curious to hear yours. Was it forecasting, churn prediction, anomaly detection, recommendation systems, causal inference, or something else? What was the biggest lesson you took away?