[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:from sklearn.neighbors import KNeighborsClassifier<br><br>knn = KNeighborsClassifier(n_neighbors=3, p=2)<br><br>#knn.fit(states.drop("state", axis="columns"), states[\'state\'])<br>knn.fit(states[["latitude", "longitude"]], states[\'state\'])<br><br><br>#trainHouses["state"] = knn.predict(trainHouses.drop(trainHouses.columns.drop(["latitude", "longitude"]), axis="columns"))<br>#prediction = knn.predict(trainHouses.drop(trainHouses.columns.drop(["latitude", "longitude"]), axis="columns"))<br>prediction = knn.predict(trainHouses[["latitude", "longitude"]])<br><br>#print(prediction.value_counts())<br>print(np.unique(prediction,return_counts=True))'}, {'role': 'assistant', 'content': "['modelling']"}]