Pass/Fail Classifier

Using the notes' own pass/fail dataset (hours studied + attendance), train a LogisticRegression model and calculate its accuracy on the held-out test set.

Approach: reuse the exact dataset, testsize, and randomstate from the notes' mini project, fit LogisticRegression, predict on X_test, and print the accuracy percentage.

Input: No input.

Output: One line: Accuracy: <percentage>%, to 2 decimal places.

Example 1

Input

(none)

Output

Accuracy: 100.00%

Hint 1

accuracy_score(y_test, predictions) compares predicted labels to the real ones and returns the fraction correct.

Hint 2

Multiply by 100 and format with :.2f to print it as a percentage.

With the fixed randomstate=42 split, the same 7 training / 3 testing examples are used every run. Fitting LogisticRegression on the training portion and calling accuracyscore(y_test, predictions) on the test portion measures exactly how many of those held-out predictions matched their real pass/fail label — matching the notes' own mini-project result of 100% on this small, clearly-separable dataset.