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Which of the following is a part of Kantian ethical theory?

What are modifiable risk factors of stroke? (Select all that apply)

Scenario E: CRISP-DM End-to-EndA manager asks: “Can we reduce churn by targeting customers at risk with retention offers?”Which CRISP-DM phase is primarily about defining success criteria and the business goal?

Scenario B: Customer Churn ClassificationA subscription business wants to predict whether a customer will churn (cancel) next month.Target: churn (1 = churned, 0 = stayed).The business cares more about catching likely churners than about occasionally flagging a loyal customer.A confusion matrix on the test set (positive class = churn) is: Predicted 0 Predicted 1 Actual 0 720 80 Actual 1 150 50 Given the business goal, the most appropriate next step is:

A bar chart of total revenue by region answers which type of business question best?

Scenario B: Customer Churn Classification A subscription business wants to predict whether a customer will churn (cancel) next month. Target: churn (1 = churned, 0 = stayed). The business cares more about catching likely churners than about occasionally flagging a loyal customer. A confusion matrix on the test set (positive class = churn) is: Predicted 0Predicted 1 Actual 072080 Actual 115050 Which statement is correct?

Scenario B: Customer Churn ClassificationA subscription business wants to predict whether a customer will churn (cancel) next month.Target: churn (1 = churned, 0 = stayed).The business cares more about catching likely churners than about occasionally flagging a loyal customer.A confusion matrix on the test set (positive class = churn) is: Predicted 0 Predicted 1 Actual 0 720 80 Actual 1 150 50 How many false negatives are there?

What does “ADA ” stand for?

Scenario B: Customer Churn ClassificationA subscription business wants to predict whether a customer will churn (cancel) next month.Target: churn (1 = churned, 0 = stayed).The business cares more about catching likely churners than about occasionally flagging a loyal customer.If you care about minimizing false positives (don’t bother loyal customers), you would emphasize:

Scenario A: Messy Retail Sales ExtractYou are analyzing a retail dataset with columns: date (string like “2025-03-01”) region (text with inconsistent capitalization and extra spaces) channel (“Online” or “Store”) price (numeric, may contain missing values) quantity (integer) Assume each row is an order line. You will clean the data and compute KPIs.You create revenue = price * quantity. Which is the correct pandas line?