[{'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:hide_code\nplt.figure(figsize=(12,5))\nx = features[\'PTRATIO\']\nx1 = np.array([[v,1] for v in x])\n(slope,bias),_,_,_ = np.linalg.lstsq(x1,y1)\ny_lin = x1 * slope + bias\nplt.scatter(x1, y1, color=\'#348ABD\', marker=\'*\', facecolors=\'none\', label=\'Original data\')\nplt.scatter(x1, y_lin, color=\'#104E8B\', marker=\'v\', facecolors=\'none\', label=\'Fitted line\')\nplt.xlabel("Feature \'PTRATIO\'")\nplt.ylabel("Prices")\nplt.title(\'Boston Housing Data\')\nplt.legend()'}, {'role': 'assistant', 'content': "['data_exploration', 'modelling']"}]