fake_news_code_d.py

# -*- coding: utf-8 -*-
"""fake news code_d.ipynb

Automatically generated by Colaboratory.



# 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

# Modelling Algorithms
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import PassiveAggressiveClassifier

# Modelling Helpers
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import metrics

# Computations
import itertools

# Visualization
import matplotlib.pyplot as plt

test  = pd.read_csv ("test.csv")
submit  = pd.read_csv ("submit.csv")

train = pd.read_csv("test.csv")

train.head()

print(f"Train Shape : {train.shape}")
print(f"Test Shape : {test.shape}")
print(f"Submit Shape : {submit.shape}")

train.info()

train.isnull().sum()

train.dtypes.value_counts()

test=test.fillna(' ')
train=train.fillna(' ')

# Create a column with all the data available
test['total']=test['title']+' '+test['author']+' '+test['text']
train['total']=train['title']+' '+train['author']+' '+train['text']

# Have a glance at our training set
train.info()
train.head()





"""Hi WAIT ............

This is Just Small Demo. 

Full Code is long... 

Full Project is Available with Porject Report and PPT.

##Mail me at **Vatshayan007@mail.com** to get this project.
"""