only, To determine the accuracy of the class-based prediction method on the test set, follow these steps: Calculate Survival Rate per Class: Determine the survival rate for each passenger class in the training set or a provided dataset. This involves calculating the proportion of survivors in each class (e.g., 1st, 2nd, and 3rd). Make Predictions: For each class, if the survival rate is greater than 0.5, predict survival for passengers in that class. If the survival rate is 0.5 or lower, predict death for passengers in that class. Evaluate Accuracy: Once predictions are made for the test set, compare these predictions to the actual outcomes (i.e., whether each passenger survived or not). Accuracy is calculated as the number of correct predictions divided by the total number of test cases. Example: Suppose you've calculated that the survival rates are: 1st Class: 0.62 2nd Class: 0.47 3rd Class: 0.24 The predictions would be: Predict survival for 1st Class. Predict death for 2nd and 3rd Class. To find accuracy, imagine you have test data results and your predictions matched, say, 200 out of 300 cases correctly, your accuracy would be 200 300 0.667 300 200 0.667 or 66.7%. Data Requirement: To execute this, you need the survival data per class from the training data and corresponding actual survival outcomes in the test set to compute the accuracy accurately. If you have specific numbers from a dataset or example calculations, these can be plu