[{'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:#input data<br>train_df=pd.read_json(\'../input/train.json\')<br>test_df=pd.read_json(\'../input/test.json\')<br>train_df.head()<br>#removing outliers<br><br>test_df["bathrooms"].loc[19671] = 1.5<br>test_df["bathrooms"].loc[22977] = 2.0<br>test_df["bathrooms"].loc[63719] = 2.0<br><br>ulimit = np.percentile(train_df.price.values, 99)<br>train_df[\'price\'].ix[train_df[\'price\']>ulimit] = ulimit<br><br>ulimit = np.percentile(test_df.price.values, 99)<br>test_df[\'price\'].ix[test_df[\'price\']>ulimit] = ulimit<br><br>train_df["logprice"] = np.log(train_df["price"])<br>test_df["logprice"] = np.log(test_df["price"])<br><br># count of "photos"<br>train_df["num_photos"] = train_df["photos"].apply(len)<br>test_df["num_photos"] = test_df["photos"].apply(len)<br><br>train_df["num_features"] = train_df["features"].apply(len)<br>test_df["num_features"] = test_df["features"].apply(len)<br><br>train_df["num_description_words"] = train_df["description"].apply(lambda x: len(x.split(" ")))<br>test_df["num_description_words"] = test_df["description"].apply(lambda x: len(x.split(" ")))<br><br><br><br>train_df["pos"] = train_df.longitude.round(3).astype(str) + \'_\' + train_df.latitude.round(3).astype(str)<br>test_df["pos"] = test_df.longitude.round(3).astype(str) + \'_\' + test_df.latitude.round(3).astype(str)<br><br>vals = train_df[\'pos\'].value_counts()<br>dvals = vals.to_dict()<br>train_df["density"] = train_df[\'pos\'].apply(lambda x: dvals.get(x, vals.min()))<br>test_df["density"] = test_df[\'pos\'].apply(lambda x: dvals.get(x, vals.min()))<br><br><br>#basic features<br>train_df["price_t"] =train_df["price"]/train_df["bedrooms"]<br>test_df["price_t"] = test_df["price"]/test_df["bedrooms"] <br>train_df["room_sum"] = train_df["bedrooms"]+train_df["bathrooms"] <br>test_df["room_sum"] = test_df["bedrooms"]+test_df["bathrooms"] <br><br><br>train_df[\'price_per_room\'] = train_df[\'price\']/train_df[\'room_sum\']<br>test_df[\'price_per_room\'] = test_df[\'price\']/test_df[\'room_sum\']<br><br>image_date = pd.read_csv(\'../input/listing_image_time.csv\')<br><br># rename columns so you can join tables later on<br>image_date.columns = ["listing_id", "time_stamp"]<br><br># reassign the only one timestamp from April, all others from Oct/Nov<br>image_date.loc[80240,"time_stamp"] = 1478129766 <br><br>image_date["img_date"]                  = pd.to_datetime(image_date["time_stamp"], unit="s")<br>image_date["img_days_passed"]           = (image_date["img_date"].max() - image_date["img_date"]).astype("timedelta64[D]").astype(int)<br>image_date["img_date_month"]            = image_date["img_date"].dt.month<br>image_date["img_date_week"]             = image_date["img_date"].dt.week<br>image_date["img_date_day"]              = image_date["img_date"].dt.day<br>image_date["img_date_dayofweek"]        = image_date["img_date"].dt.dayofweek<br>image_date["img_date_dayofyear"]        = image_date["img_date"].dt.dayofyear<br>image_date["img_date_hour"]             = image_date["img_date"].dt.hour<br>image_date["img_date_monthBeginMidEnd"] = image_date["img_date_day"].apply(lambda x: 1 if x<10 else 2 if x<20 else 3)<br><br>train_df = pd.merge(train_df, image_date, on="listing_id", how="left")<br>test_df = pd.merge(test_df, image_date, on="listing_id", how="left")<br><br>features_to_use=["bathrooms", "bedrooms", "price_t","room_sum","latitude","longitude","num_photos","density","logprice","num_features","num_description_words","price_per_room","listing_id","img_date"]<br>print(train_df[\'price\'].head())<br>'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]