import numpy as np #machine learning tool used for efficient array processing

import pandas as pd #machine learning tool used for data sets and data frames

from sklearn.model

$\_$

selection import train

$\_$

test

$\_$

split #traditional machine learning

from sklearn.feature

$\_$

extraction.text import TfidfVectorizer#text is converted into vectrorizeor

$($

numbers

$)$

$$to feed into computer

#tf

$-$

how much times a term is repeated,idf

$-$

inverse documentry frequency

$-$

no of documents

$/$

no of documents has the term

from sklearn.linear

$\_$

model import PassiveAggressiveClassifier # this is for text classification

from sklearn.metrics import accuracy

$\_$

score, confusion

$\_$

matrix #for result

# Read the data

df

$=$

$$pd

$.$

read

$\_$

csv

$($

$\text{'}$

$/$

content

$/$

fake

$\_$

or

$\_$

real

$\_$

news.csv

$\text{'}$

$)$

$$#reading the data and lebelling them,for accuracy

# Get shape and head

print

$($

df

$.$

shape

$)$

#This line prints the shape of the DataFrame df

$,$

$$which represents the number of rows and columns in the DataFrame.

print

$($

df

$.$

head

$($

$)$

$)$

# This line prints the first few rows of the DataFrame df

$.$

$$By default, it prints the first

$5$

$$rows

#DataFlair

$-$

$$Get the labels

labels

$=$

df

$.$

label

labels.head

$($

$)$

class TextClassification:

$$ def

$\_$

$\_$

init

$\_$

$\_$

$($

self

$,$

$$df

$,$

$$labels

$)$

:#here we split the data into train and test so that we can see the accurcy

$$ self.df

$=$

$$df #df

$($

pandas

$.$

DataFrame

$)$

:The DataFrame containing the text data and labels.

$$ self.labels

$=$

$$labels #The Series containing the labels

$($

target variable

$)$

$$ self.x

$\_$

train, self.x

$\_$

test, self.y

$\_$

train, self.y

$\_$

test

$=$

$$train

$\_$

test

$\_$

split

$($

df

$[$

$\text{'}$

text

$\text{'}$

$]$

$,$

$$labels$,$ test

$\_$

size

$=$

$0$

$.$

$2$

$,$

$$random

$\_$

state

$=$

$7$

$)$

# Split data into training and testing sets

$($

$8$

$0$

$\%$

$$train$,$

$2$

$0$

$\%$

$$test

$)$

$$ self.tfidf

$\_$

vectorizer

$=$

$$TfidfVectorizer

$($

stop

$\_$

words

$=$

'english', max

$\_$

df

$=$

$0$

$.$

$7$

$)$

# Create a TF

$-$

IDF vectorizer with English stop words removed and a maximum document frequency threshold of

$0$

$.$

$7$

$$ self.tfidf

$\_$

train

$=$

$$None

$$ self.tfidf

$\_$

test

$=$

$$None

$$ self.pac

$=$

$$PassiveAggressiveClassifier

$($

max

$\_$

iter

$=$

$5$

$0$

$)$

# Instantiate a PassiveAggressiveClassifier with a maximum number of iterations of

$5$

$0$

$$ def preprocess

$\_$

data

$($

self

$)$

:#Preprocesses the text data using TF

$-$

IDF vectorization.

$$ self.tfidf

$\_$

train

$=$

$$self$.$tfidf

$\_$

vectorizer.fit

$\_$

transform

$($

self

$.$

x

$\_$

train

$)$

$$ self.tfidf

$\_$

test

$=$

$$self$.$tfidf

$\_$

vectorizer.transform

$($

self

$.$

x

$\_$

test

$)$

$$ def train

$\_$

model

$($

self

$)$

:#Trains the text classification model using the PassiveAggressiveClassifier.

$$ self.pac.fit

$($

self

$.$

tfidf

$\_$

train, self.y

$\_$

train

$)$

$$ def evaluate

$\_$

model

$($

self

$)$

:#Evaluates the trained model's performance using accuracy and confusion matrix.

$$ y

$\_$

pred

$=$

$$self$.$pac.predict

$($

self

$.$

tfidf

$\_$

test

$)$

$$ score

$=$

$$accuracy

$\_$

score

$($

self

$.$

y

$\_$

test, y

$\_$

pred

$)$

$$ print

$($

f

$\text{'}$

Accuracy:

$\{$

round

$($

score

$*$

$1$

$0$

$0$

$,$

$2$

$)$

$\}$

$\%$

$\text{'}$

$)$

$$ confusion

$\_$

mat

$=$

$$confusion

$\_$

matrix

$($

self

$.$

y

$\_$

test, y

$\_$

pred, labels

$=$

$[$

$\text{'}$

FAKE

$\text{'}$

$,$

$\text{'}$REAL$\text{'}$

$]$

$)$

$$ print

$($

$"$

Confusion Matrix:"

$)$

$$ print

$($

confusion

$\_$

mat

$)$

if

$\_$

$\_$

name

$\_$

$\_$

$=$

$=$

$"$

$\_$

$\_$

main

$\_$

$\_$

$"$

:

$$ # Sample usage

$$ df

$=$

$$pd

$.$

read

$\_$

csv

$($

$\text{'}$

$/$

content

$/$

fake

$\_$

or

$\_$

real

$\_$

news.csv

$\text{'}$

$)$

$$ labels

$=$

$$df

$[$

$\text{'}$

label

$\text{'}$

$]$

$$ # Create an instance of TextClassification

$$ classifier

$=$

$$TextClassification

$($

df

$,$

$$labels

$)$

$$ # Preprocess the data

$$ classifier.preprocess

$\_$

data

$($

$)$

$$ # Train the model

$$ classifier.train

$\_$

model

$($

$)$

$$ # Evaluate the model

$$ classifier.evaluate

$\_$

model

$($

$)$

$.$

$.$

this is code and output screenshot for fake news detection using python pls do provide with detailed ellaborate content for Abstract

Introduction

Methodology

Results

$($

Results Screenshot

$)$

Conclusion for this project in detail very big