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# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

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

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
/kaggle/input/fake-news-data/train.csv /kaggle/input/fake-news-data/test.csv
import pandas as pd
df=pd.read_csv('/kaggle/input/fake-news-data/train.csv')
df.head()
###Drop Nan Values
df=df.dropna()
#get the dependent feature
X=df.drop('label',axis=1)
# Get the Dependent features
y=df['label']