Maduro Cleaning is a small organization that provides cleaning services to both residential and commercial clients. As a small organization, the owner assigns crews of two, three, or four employees to jobs each morning but the owner does not have a good method of determining a reasonable amount of time that each cleaning should take. The owner decided to keep data on job times and crew sizes in the hopes of developing a productivity measure.
Address the following requirements:
• Calculate which of the crew sizes has the best productivity per worker, and explain your method.
• Evaluate your outcome and the possible reasons that would explain those results.
• Project what the productivity might be for a crew size of five and explain your reasoning.
Crew Size Avg: Productivity per Crew
• 2: 3765 square meters per day
• 3: 4915 square meters per day
• 4: 6309 square meters per day
datasets that will be used for the training of the recommendation system are called MMTF-14K (Deldjoo), MovieLens 20M (reference), and UC Irvine Machine Learning Lab’s Movie Data Set, which has data on the cast of over 10,000 movies. 3.4 Summarization During the summarization process, video segments are ranked based on computed similarity measures between the user profile and the movie features. Personalized movie summarization can be seen as “the process of measuring the similarity score of each video segment for the given user preferences and selecting those top ranked segments that will increase the cumulative similarity score of the summary” (Kannan et al., 2015). First, the similarity between each shot and the user preferences on actor appearance, genre, and visual descriptors is calculated using cosine similarity measures. Each shot is stored as a vector of its features in a high-dimensional space, after which the angles between the vectors are calculated as the cosine similarity between the vectors. After this, user profiles are created based on their ratings on the same features on movies and the similarity between a shot and a user is computed similarly. This should return a ranked list of shots to select for that specific user. 3.5 Evaluation In accordance with previous studies on automatically generated movie trailers, a qualitative user study will be performed to evaluate the summarization system. This presents the “cold-start” problem of recommendation, as there will be no data on the users in question. To alleviate this problem, the most direct way is to make a rapid profile of a new user by asking for explicit ratings after presenting a number of movies to the user. In an online questionnaire format, 20-50 users will first be given 20 movies to>GET ANSWER