What crucial type of data is missing from a customer purchase history spreadsheet intended for predictions?

The missing crucial type of data from a customer purchase history spreadsheet intended for predictions is the label. In the context of machine learning and predictive analytics, a label represents the outcome or result that the model is trying to predict based on the input data. For instance, in customer purchase history, the label could be a future purchase or a customer churn indicator. Having labels allows the model to learn from historical data to make accurate predictions about future shopping behavior.

When working with labeled data, the model can assess the relationship between input features (like previous purchases, shopping frequency, etc.) and the desired output (the label). This learning process is what enables the model to generalize and make predictions on unseen data. In the absence of labels, predictive models cannot effectively learn or assess the correlations required for accurate forecasting, rendering the data less useful for predictive analysis.