Problem:Using a dataset of user-movie ratings (e.g., from IMDb or Kaggle's MovieLens dataset), construct auser-item rating matrix where rows represent users and columns represent movies. Solve thefollowing:•Apply Matrix Factorization to predict missing ratings using Singular Value Decomposition (SVD).•Reconstruct the original matrix and calculate the Frobenius norm of the difference.•Identify the top 5 movies recommended for a specific user based on predicted ratings.Data Source: Kaggle MovieLens Dataset or IMDb user ratings.

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College Algebra (MindTap Course List)

12th Edition

ISBN:9781305652231

Author:R. David Gustafson, Jeff Hughes

Publisher:R. David Gustafson, Jeff Hughes

Chapter6: Linear Systems

Section6.2: Guassian Elimination And Matrix Methods

Problem 81E

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The data set hemorrhage.sav contains a sample of 100 low birth weight infants born in Boston, Massachusetts. Germinal matrix hemorrhage (gmh) is a dichotomos variable that coded as 1 if the baby had a hemorrhage and 0 if the baby did not have a hemorrhage. You wish to know if head circumference, systolic blood pressure (sbp), and gender predicts hemorrhage. (See the attached Image) Determine the significance of the independent variables in the model, provide the test statistics and p-values to support your response.
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