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test-algorithm-1.py

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@@ -33,15 +33,9 @@
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# See what the classifier predicts for some new tweets:
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predictions = nb.predict(counts)
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<<<<<<< HEAD:test-algorithm-1.py
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correct= sum(predictions == fixed_target)
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incorrect= sum(predictions != fixed_target)
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acc = correct/(len(fixed_target))
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print(acc)
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=======
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correct_predictions = sum(predictions == target)
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incorrect_predictions = 9092 - correct_predictions # (there are 9,092 tweets in the csv)
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print('# of correct predictions: ' + str(correct_predictions))
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print('# of incorrect predictions: ' + str(incorrect_predictions))
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print('Percent correct: ' + str(100.0 * correct_predictions / (correct_predictions + incorrect_predictions)))
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>>>>>>> a85337990f2d389bd846bc203e53a58cbab4e5a7:test_algorithm_1.py

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