# data analysis and wrangling
import pandas as pd
import numpy as np
import random as rnd

#visualization 
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline

#machine learning
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier


train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]


print (train_df.columns.values)

#preview data
train_df.head()
train_df.tail()