from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import itertools

import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#from pylab import rcParams
#import matplotlib

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

train = pd.read_csv('train.csv')
print('Shape of the train data with all features:', train.shape)
train = train.select_dtypes(exclude=['object'])
print("")
print('Shape of the train data with numerical features:', train.shape)
train.drop('Id',axis = 1, inplace = True)
train.fillna(0,inplace=True)

test = pd.read_csv('test.csv')
test = test.select_dtypes(exclude=['object'])
ID = test.Id
test.fillna(0,inplace=True)
test.drop('Id',axis = 1, inplace = True)

print("")
print("List of features contained our dataset:",list(train.columns))
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import itertools

import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#from pylab import rcParams
#import matplotlib

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

train = pd.read_csv('train.csv')
print('Shape of the train data with all features:', train.shape)
train = train.select_dtypes(exclude=['object'])
print("")
print('Shape of the train data with numerical features:', train.shape)
train.drop('Id',axis = 1, inplace = True)
train.fillna(0,inplace=True)

test = pd.read_csv('test.csv')
test = test.select_dtypes(exclude=['object'])
ID = test.Id
test.fillna(0,inplace=True)
test.drop('Id',axis = 1, inplace = True)

print("")
print("List of features contained our dataset:",list(train.columns))
#import matplotlib.pyplot as plt #from pylab import rcParams #import matplotlib