<jupyter_start><jupyter_text>Importing KNN<jupyter_code>from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
X = iris.data
y = iris.target
knn.fit(X,y)
print(knn.predict( [ [ 5.1 , 3.5 , 1.4 , 0.2 ]] ))
print(knn.predict( [ [ 5.9 , 3 ,  5.1 , 1.8 ]] ))<jupyter_output>[2]
{'fraction_non_alphanumeric': 0.17363344051446947, 'fraction_numerical': 0.05466237942122187, 'mean_word_length': 4.886792452830188, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '55472', 'license': 'MIT', 'n_tokens_mistral': 162, 'n_tokens_neox': 149, 'n_words': 23, 'path': 'ML with Jupiter Files/IrisDataset.ipynb', 'repo_name': 'ajsamant/SampleCodeAjinkya', 'size': '311'}