[{'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:expected_street_types = ["Street", "Avenue", "Boulevard", "Drive", "Court", "Place", "Square", "Lane", "Road",\n"Trail", "Parkway", "Commons"]\nmapping = { "St": "Street",\n"St.": "Street",\n\'AVE\': \'Avenue\',\n\'Ave\': \'Avenue\',\n\'Ave.\': \'Avenue\',\n\'Av.\': \'Avenue\',\n\'ave\': \'Avenue\',\n\'Blvd\': \'Boulevard\',\n\'Blvd.\': \'Boulevard\',\n\'boulevard\': \'Boulevard\',\n\'CT\': \'Court\',\n\'Ct\': \'Court\',\n\'Dr\': \'Drive\',\n\'Dr.\': \'Drive\',\n\'E\': \'East\',\n\'E.Division\': \'East Division\',\n\'FI\': \'Fox Drive\',\n\'Hwy\': \'Highway\',\n\'K10\': \'NE 8th Street\',\n\'MainStreet\': \'N Main Street\',\n\'N\': \'North\',\n\'NE\': \'Northeast\',\n\'NW\': \'Northwest\',\n\'nw\': \'Northwest\',\n\'PL\': \'Place\',\n\'Pl\': \'Place\',\n\'Rd\': \'Road\',\n\'RD\': \'Road\',\n\'Rd.\': \'Road\',\n\'S\': \'South\',\n\'S.\': \'South\',\n\'S.E.\': \'Southeast\',\n\'SE\': \'Southeast\',\n\'ST\': \'Street\',\n\'SW\': \'Southwest\',\n\'SW,\': \'Southwest\',\n\'Se\': \'Southeast\',\n\'southeast\': \'Southeast\',\n\'St\': \'Street\',\n\'st\': \'Street\',\n\'street\': \'Street\',\n\'St.\': \'Street\',\n\'Ter\': \'Terrace\',\n\'W\': \'West\',\n\'west\': \'West\',\n\'WA\': \'17625 140th Avenue Southeast\',\n\'WA)\': \'US 101\',\n\'WY\': \'Way\'\n}\n\'\'\'\nReference:  Udacity\n\'\'\'\ndef street_type(street_types, street_name):\nm = street_type_re.search(street_name)\nif m:\nstreet_type = m.group()\nif street_type not in expected_street_types:\nstreet_types[street_type].add(street_name)\ndef is_street_name(elem):\nreturn (elem.attrib[\'k\'] == "addr:street")\ndef print_sorted_dict(d):\nkeys = d.keys()\nkeys = sorted(keys, key=lambda s: s.lower())\nfor k in keys:\nv=d[k]\nprint(\'%s: %d\' % (k, v))\ndef audit_streets(osm_file):\nstreet_types = defaultdict(set)\nfor event, elem in xET.iterparse(osm_file, events=("start",)):\nif elem.tag == "node" or elem.tag == "way":\nfor tag in elem.iter("tag"):\nif is_street_name(tag):\nstreet_type(street_types, tag.attrib[\'v\'])\nosm_file.close()\nreturn street_types\ndef update_name(name, mapping):\nn = street_type_re.search(name)\nn = n.group()\nfor m in mapping:\nif n == m:\nname = name[:-len(n)] + mapping[m]\nreturn name\ndef audit_update_street_types():\nst_types = audit_streets(open(SAMPLE_NAME, "rb"))\nprint(\'Total Ways: \', len(st_types)) #Total Number of street types\nfor st_type, ways in st_types.items():\nfor name in ways:\nbetter_name = update_name(name, mapping)\nif name != better_name:\nprint(name, "=>", better_name)\nname = better_name\ndef is_zipcode(elem):\nreturn (elem.attrib[\'k\'] == \'addr:postcode\')\ndef incorrect_zips():\nzipcodes = {}\nfor event, elem in xET.iterparse(SAMPLE_FILE, events=(\'start\',)):\nif elem.tag == "node" or elem.tag == "way":\nfor tag in elem.iter("tag"):\nif is_zipcode(tag): #and tag.attrib[\'v\'].startswith(\'98\')\nzipc = tag.attrib[\'v\']\nif zipc not in zipcodes.keys():\nzipcodes[zipc] = 1\nelse:\nzipcodes[zipc] = zipcodes[zipc] + 1\nreturn zipcodes\ndef correct_zipcodes():\nfor zipcode in incorrect_zips():\ntry:\nbetter_zipcode = update_zip(zipcode)\nexcept:\nzipcode = zipcode.upper()\nif zipcode != better_zipcode:\nprint(zipcode, "=>", better_zipcode)\ndef update_zip(zipcode):\nif zipcode == "Olympia, 98501":\nzipcode = "98501"\nelif zipcode.startswith(\'V\') or zipcode.startswith(\'v\'):\nz1 = zipcode[:3]\nz2 = zipcode[-3:]\nzipcode = z1 + \' \' + z2\nzipcode = zipcode.upper()\nreturn zipcode\nprint("Corrected Zip Codes: ")\ncorrect_zipcodes()\nprint(\'\\n\')\nprint("Corrected Street Names: ")\naudit_update_street_types()'}, {'role': 'assistant', 'content': "['data_exploration', 'data_preprocessing']"}]