Marking Scheme 1. 3 Functions in a separate module file [6 A marks] a. Parameters / arguments used b. Return values used 2. Main file created [6 A marks] a. Main function created b. Main function used appropriately 3. Decision structure used [2 A marks] 4. Repetition structure used [2 A marks] 5. Comment/header/style [2 A marks] 6. Purpose of code is clear / program has a clear purpose [2 A marks] 7. Use of predefined modules (math/random) [2 A marks]

First, a quick definition of the method should be given before moving on to the three components that make it up.

First, a quick definition of the method should be given before moving on to the three components that make it up.

Let's revisit our first problem, where we want to set up a series of chess matches so we can rank six players in our class. As we did before, we will assume that everyone keeps their chess rating a private secret; however, when two players have a chess match, the person with the higher rating wins 100% of the time. But this time, we are only interested in identifying the BEST of these six players and the WORST of these six players. (We don't care about the relative ordering or ranking of the middle four players.) Your goal is to devise a comparison-based algorithm that is guaranteed to identify the player with the highest rating and the player with the lowest rating. Because you are very strong at Algorithm Design, you know how to do this in the most efficient way. Here are five statements. A. There exists an algorithm to solve this problem using 6 matches, but there does not exist an algorithm using only 5 matches. B. There exists an algorithm to solve this problem using 7 matches,…

Complete the Team class implementation. For the instance method get_win_percentage(), the formula is:team_wins / (team_wins + team_losses)

Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi

How does the "slice and dice" method work for the multiple model?

Calculate the decision parameter p for Bresenham's circle drawing method. The step-by-step presentation of Bresenham's algorithm for drawing a circle is provided.

There are 22 gloves in a drawer: 5 pairs of red gloves, 4 pairs of yellow, and 2 pairs of green. You select the gloves in the dark and can check them only after a selection has been made. What is the smallest number of gloves you need to select to have at least one matching pair in the best case? in the worst case?

Find out what value the decision parameter p should have for the Bresenham's circle drawing technique. The algorithm that Bresenham devised for drawing a circle is offered in the form of step-by-step instructions here.