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"metadata": {},
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"outputs": [],
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"source": [
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"# Learning Practice 2 for the University of Tulsa's QM-7063 Data Mining Course\n",
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"# Dimension Reduction\n",
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"# Learning Practice 3 for the University of Tulsa's QM-7063 Data Mining Course\n",
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"# Recommender Systems\n",
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"# Professor: Dr. Abdulrashid, Spring 2023\n",
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"# Noah L. Schrick - 1492657\n",
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"\n",
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"data = Dataset.load_from_df(course_melt[['Index', 'Course', 'Taken']], reader)\n",
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"trainset = data.build_full_trainset()\n",
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"\n",
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"# NOTE: The following will error. This is expected and part of the question. Explanation in the corresponding answer.\n",
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"# NOTE: The following will not work. This is expected and part of the question. Explanation in the corresponding answer.\n",
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"#sim_options = {'name': 'cosine', 'user_based': True} # compute cosine similarities between users\n",
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"#algo = KNNBasic(sim_options=sim_options)\n",
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"#algo.fit(trainset)\n"
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