Nuked Linly's splitting in mining_hq.py
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@@ -127,26 +127,3 @@ print(sample_rows.head())
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scout.dissimilarity(sample_rows)
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scout.similarity(sample_rows)
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# split the data set
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gammas_train, gammas_test = train_test_split(gammas, test_size=0.20, random_state=69)
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gammas_train.to_csv("datasets/videogames/games_train.csv", index=False)
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gammas_test.to_csv("datasets/videogames/games_test.csv", index=False)
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# kmeans pls
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gammas_train_kmeans = KMeans(n_clusters=10, random_state=420, n_init="auto").fit(
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gammas_train[["Critic_Score", "User_Score", "Total_Shipped"]]
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)
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gammas_labels = gammas_train_kmeans.labels_
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silh_score = metrics.silhouette_score(
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gammas_train[["Critic_Score", "User_Score", "Total_Shipped"]],
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gammas_labels,
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metric="euclidean",
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)
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print(silh_score)
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gammas_train["Kmean Labels"] = gammas_labels
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print(gammas_train.head())
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# Naive based
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gnb = GaussianNB()
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prediction = gnb.fit(gammas_train)
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