Creating a new binary indicator feature is a best practice for handling ‘missing not at random’ data, which this likely is (the absence of an interaction is informative). It preserves the information that the value is missing while allowing the model to use both the presence/absence and the actual date (for those who have it) as separate signals. Imputing dates with medians or placeholders introduces false data, and deleting records loses valuable information and can bias the model.

Clustering is an unsupervised exploratory method. Its initial output is a grouping of data points. The crucial analytical work that follows is profiling—analyzing the characteristics of each cluster to translate the mathematical grouping into actionable business segments. This directly addresses the stakeholder’s need for a ‘story’. The other options either dismiss the stakeholder’s valid concern or misrepresent the purpose of the technique.

With an imbalanced class problem where the positive class (fraud) is rare, high accuracy can be misleading. The model is biased toward the majority class. Adjusting the decision threshold (e.g., lowering the probability required to classify as fraud) is a direct method to increase the true positive rate (recall) for fraud, aligning the model with the business objective of catching more fraud, albeit with more false alarms. Simply retraining for accuracy won’t fix the imbalance issue.

A line chart with a forecast line and a confidence interval band is the standard and most intuitive visualization for time series forecasts. It clearly shows the historical trend, the central forecast (point estimate), and the range of probable outcomes (uncertainty). This allows decision-makers to plan for the most likely scenario while understanding the risks and potential variability. Tables are less intuitive for trends, and other charts omit critical forecast or uncertainty information.

Overfitting occurs when a model learns the noise and specific patterns of the training data too well, harming its ability to generalize to new data. The classic signature is excellent performance on training data (high training R-squared) but noticeably worse performance on unseen test data (lower test R-squared). A small coefficient or high p-value suggests the feature may not be useful, but not necessarily that it caused overfitting. A general increase in R-squared might just mean a better model.

Sensitivity analysis is a crucial technique for understanding model behavior and risk. It systematically changes input variables within plausible ranges to observe the corresponding changes in the model’s output. This helps answer questions like: ‘Which inputs drive the results the most?’ and ‘How stable is our prediction if an input value is slightly wrong?’ It’s about testing the model’s robustness, not technical implementation, stakeholder preferences, or data governance.

A p-value of 0.07 means there is a 7% probability of observing a difference as large as 5% (or larger) if there were actually no real difference between the variants (null hypothesis is true). While suggestive, it does not meet the common threshold for statistical significance (e.g., p < 0.05). The most responsible action is not to declare a winner but to collect more data to reduce uncertainty and achieve a more definitive result, thereby avoiding a potentially false positive rollout.

A correlation coefficient (like Pearson’s r) near 0 (0.02 is very close to 0) indicates the absence of a linear relationship between the two variables. Changes in one variable do not predictably correspond to changes in the other in a linear fashion. It does not imply an error; it is a valid and common result. It tells the analyst not to expect one variable to linearly explain the other.

This splitting strategy is fundamental to rigorous model development and evaluation. The training set is for learning parameters. The validation set is used during development to tune hyperparameters and select between models—this process can indirectly ‘fit’ to the validation set. The test set is held out completely until the very end and used only once to provide an unbiased estimate of how the final chosen model will perform on new, unseen data, simulating a real-world deployment.

Creating a new binary indicator feature is a best practice for handling ‘missing not at random’ data, which this likely is (the absence of an interaction is informative). It preserves the information that the value is missing while allowing the model to use both the presence/absence and the actual date (for those who have it) as separate signals. Imputing dates with medians or placeholders introduces false data, and deleting records loses valuable information and can bias the model.0