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import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option('display.max_columns', 100)
from lightgbm import LGBMRegressor
SEED = 47

PATH = '../input/30-days-of-ml/'
df_train = pd.read_csv(PATH + '/train.csv')
df_test = pd.read_csv(PATH + '/test.csv')
df_sub = pd.read_csv(PATH + '/sample_submission.csv')

target = df_train['target']
features = df_train.drop(['id', 'target'], axis=1)

features.head()