You are given a Pandas DataFrame `customer_df` with the following columns: TenureMonths (numeric) MonthlyCharges (numeric, dollars) NumSupportCalls (integer) HasPromoCode (0 or 1) Churned (0 or 1 — the TARGET) Write a short Python script (roughly 10-15 lines) that does ALL of the following, in order: (1) Imports from sklearn everything you will need (train/test split, logistic regression, and the metrics to print a confusion matrix and a classification report). (2) Prepares the feature matrix X (using the four predictor columns) and the target y. (3) Splits the data into 80% training and 20% test, with a fixed random_state for reproducibility and with the class balance preserved in both splits. (4) Creates a LogisticRegression model (allowing enough iterations to converge, and with a setting appropriate for an imbalanced target) and fits it on the training data. (5) Uses the fitted model to produce predicted class labels on the test set. (6) Prints both the confusion matrix and the classification report for the test-set predictions. Use the exact function and method names you learned in class. Short variable names are fine.

Given a DataFrame df with a ‘City’ column, which expression returns a Series listing how many rows fall into each city?

Which chart type is best for comparing the average sales across 5 product categories?

Which statement about the p-value is CORRECT?

TBD2

TBD1

Tube Feeding Order Todd is a 39-year-old male with severe stroke, NPO × 5 days, functional GI tract, normal renal function, and no indication for fluid restriction. 1. Which formula is MOST appropriate for Todd?

7. Based on Todd’s case study, which micronutrient concern is most relevant at this time?

Which of the following best describes Todd’s current nutrition risk?

What is the most appropriate protein requirement for Todd?