noah/QM-7063-Learning-Practice-3
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Noah L. Schrick 2023-02-12 18:59:05 -06:00
parent 1e2af7d33c
commit baa5d425bf
1 changed files with 4 additions and 3 deletions
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Schrick-Noah_Learning-Practice-3.ipynb
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@ -28,7 +28,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Problem 14.1" "# Problem 14.1\n",
"This is a good approach for exploring associative relationships between customers. Since there is company data mixed with demographic data, the association rules can yield better results and demonstrate better associations since purchases can be examined with respect to age, location, number of dependents, and any other demographic data available."
] ]
}, },
{ {
@ -71,9 +72,9 @@
"courses_df = pd.read_csv('Coursetopics.csv')\n", "courses_df = pd.read_csv('Coursetopics.csv')\n",
"\n", "\n",
"reader = Reader(rating_scale=(0, 1))\n", "reader = Reader(rating_scale=(0, 1))\n",
"data = Dataset.load_from_df(ratings[['customerID', 'movieID', 'rating']], reader)\n", "data = Dataset.load_from_df(courses_df['customerID', 'movieID', 'rating']], reader)\n",
"trainset = data.build_full_trainset()\n", "trainset = data.build_full_trainset()\n",
"sim_options = {'name': 'cosine', 'user_based': False} # compute cosine similarities between items\n", "sim_options = {'name': 'cosine', 'user_based': True} # compute cosine similarities between items\n",
"algo = KNNBasic(sim_options=sim_options)\n", "algo = KNNBasic(sim_options=sim_options)\n",
"algo.fit(trainset)\n", "algo.fit(trainset)\n",
"pred = algo.predict(str(823519), str(30), r_ui=4, verbose=True)" "pred = algo.predict(str(823519), str(30), r_ui=4, verbose=True)"