🔥Fake News Detection Using Machine Learning Machine Learning Projects In Python Simplilearn

foreign
today we will take you through a hands
of lab demo of how to detect fake news
using machine learning before we start I
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great I think we can get started
so in today's session
we will discuss what fake news is and at
the end we will do a hands of lab demo
of how to detect fake news using machine
learning
before we move on to the programming
part let's discuss first what fake news
is and proceed further for the same
what is fake news
polls are misleading information that is
reported as news is called fake news a
common goal or fake news is to harm
someone or something reputation or to
profit through advertising
the term fake news was first used in
1890s a time when dramatic newspaper
reports were common
even though incorrect information has
always been disamented throughout
history
however the phrase has no clear
definition and is often used to refer to
all misleading information high profile
individuals have also used it to refer
to any news that is not favorable to
them
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description box below now let's move to
our programming part
so first we will open a command prompt
to write a command to open Jupiter
notebook so here we will write Jupiter
notebook
Center
and here I have to select new python
kernel file
okay so this is how the kernel look
likes
so first we will import some major
libraries of python so here I will write
import
ance as PD
and import
numpy as NP
then
import
c bond
that's SNS
and import
SK learn
dot model selection
port
train underscore
test underscore split
before that
I will import
matplotlib
dot Pi plot
as PLT
okay then
I will write here from SK learn
Dot
Matrix
import
accuracy
or
than from
SK learn
dot matrix
Airport
classification
to report
at Port
Ari then import
string
okay
then press enter
so it is saying
okay here I have to write from
everything seems good
loading let's see
okay till then number is a python
Library used for working with arrays
which also has function for working with
domain of lineal algebra and matrices
it is an open source project and you can
use it freely
number stand for numerical python
pandas so panda is a software Library
written for Python programming language
for data manipulation and Analysis in
particular it offers data structure and
operation for manipulating numerical
tables and Time series
then Seaborn
an open source python Library based on
matplotlib is called C bone it is
utilized for data exploration and data
visualization
with data frames and the pandas Library
c-bond functions with ease
than matplotlib for Python and its
numerical extension numpy
matplotlib is a cross platform for the
data visualization and graphical
charting package
as a result it presents a strong open
source suitable formatlab
the apis for matplotlib allow
programmers to incorporate graphs into
GUI applications then this train test
split we may build our training data and
the test data with the aid of SQL and
train test split function
this is so because the original data set
often serves as both the training data
and the test data starting with a single
data set we divide it into two data sets
to obtain the information needed to
create a model like hone and test
accuracy score the accuracy score is
used to Gorge the model's Effectiveness
by calculating the ratio of total true
positive to Total to negative across all
the model prediction
this re regular expression
the function in the model allow you to
determine whether a given text fits a
given regular occupation or not
which is known as re
okay then string a collection of letters
words or other character is called a
string it is one of the basic data
structure that serves as the foundation
of manipulating data
the Str class is a built-in string class
in Python because python strings are
immutable they cannot be modified after
they have been formed
okay so now let's import the data set we
will be going to import two data set one
for the fake news and one for the True
News or you can say not fake news
okay
so I will write here
EF underscore
big
question
PD Dot
read underscore CSV
or what can I say TF fake okay
it underscore fake
okay
then
pick
dot CSV
you can download this data set from the
description box below
then data Dot
true
equals to PD dot read
underscore CSV
sorry CSG
then
fake news sorry true
dot CSV
okay then press enter
so these are the two data set
you can download these data set from the
description box below so let's see the
board data set okay
then I will write here data underscore
fake
dot head
so this is the fake data okay then
data underscore true
Dot
and this is the two data
okay this is not fake so if you want to
see your top five rows of the particular
data set you can use head
and if you want to see the last five
rows of the data set you can use tail
instead of head
okay
so let me give some space for the better
visual
so now we will insert column class as a
Target feature okay then I will write
here data
let's go fake
Plus
equals to zero
then
Theta underscore true
and
Plus
one
okay
then
I will write here data underscore fake
dot shape
and data underscore true
dot ship
okay then press enter
so the shape method return the shape of
an array the shape is a tuple of
integers these number represent the
length of the corresponding array
dimension in other words a tuple
containing the quantities of entries on
each axis is an array shape dimension so
what's the meaning of shape
in the fake world
in this data set we have two three four
eight one rows and five columns and in
this data set true we have two one four
one seven rows and five column okay so
these are the rows column rows column
for the particular data set
so now let's move
and let's remove the last 10 rows for
the manual testing okay
then I will write here data underscore
speak
let's go manual
testing
verse 2. data underscore fake
dot tail
was it last 10 rows I have to write here
10.
okay so for I
in range
two three four
eight one
sorry zero
comma 2 3
4 7 0
comma minus 1.
okay
and
TF underscore not DF data
underscore fake
dot drop
one
here instead of one I can write here I
comma
this is equals to zero
place
equals to true
then
data
not here
data underscore
same I will write for I will copy from
here
and I will paste it here and I will make
the particular changes
so here I can write true
that I can write true
okay
then I have to change a number
two one
six
right
2 1 4 0 6
-1
same
so press enter
X is equals to zero
alert since X maybe you mean double zero
or of this
okay we will put here double course
and I'm putting this
take dot drop i x is equal 0 okay in
place okay
and also write equals to a question
yeah
so
okay access is not defined
so now it's working so
let me see
now
did the
underscore
fake dot shape
okay
and data dot true
on
data underscore true
dot shape
as you can see
10 rows are deleted from each data set
so I will write here data underscore
fake underscore manual
testing
yeah
class
equals to zero
and data underscore
true
let's go
manual underscore testing
plus equals to
one
okay
just ignore this warning
and
let's see
data underscore
fake underscore
manual
testing dot head
as you can see we have this
and then data dot sorry underscore true
underscore
manual
testing
dot at
10.
this is this is done uh true data set
so here I will merge data
let us go merge
pursue
PD Dot
concat
concat is used for the concatenation
data underscore fake
data underscore
comma
axis
equals to zero
then data underscore merge
dot head
the top 10 rows
yeah
as you can see the data is merged
here
okay
first it will come for the fake news and
then with that for the True News
and let's merge true and fake data
frames
okay
we did this and
let's merge the column then data dot
merge
Dot columns or let's see the columns
it is not defined what are the data
underscore much
these are the column same title tag
subject date class okay
now
let's remove those columns which are not
required for the further process so here
I will write data underscore
or request to
data underscore merge
crop
title we don't need
that
subject
we don't need then
so one
so let's check some null values
it's giving here
because of this
that's good then data
dot is null
dot sum
Center
so no null values
okay
then
let's do the random shuffling of the
data frames okay
for that we have to write here data
equals to
data dot sample
one
then
data
okay
data
dot hat
now you can see here the random
shuffling is done
and one for the
true data reset and zero for the fake
news one okay
then
let me write here data Dot
reset
underscore index
place
because you
true
a dot dot drop
comma X is
equals to one
then comma in place
equals to true
okay
then let me see columns now data Dot
columns
so here we have two columns only rest we
have deleted
okay
so
let me see data dot at
yeah
everything seems good
let's proceed further and let's create a
function to process the text okay
for that I will write here
but
okay
you can use any name
text
and text equal to
text Dot lower
okay
and text
equal to re dot for the substring
remove these things
from the
datas
okay
so for that I'm writing here
comma
okay
then text equals to
re Dot substring
comma
comma text
okay
then I have to write text equals to
r dot substring
to www
Dot
S Plus
comma
comma text
okay then text equals to
re Dot substring
then
oh
comma
yeah
then
text equals to
re Dot substring
and
percentage
as
again percentage or
RG dot SK function
right here string
dot punctuation
comma
and comma then text
right
then text equals to re Dot substring
and and
comma
text equal to
re Dot
substring
right here
and again d
that again
then comma
and again
texture
okay then at the end after idea return
text so everything like uh this these
type of special character will be
removed from the data set
okay let's run this let's see
yeah so here I will add DF sorry not DF
data
data
then
text
pursue
data
dot apply
to the function name what part word opt
okay
and press enter
yeah
so now let's define the dependent and
independent variables okay x equals to
data
text
and Y equals to
data
class
okay
then splitting training and testing data
okay sorry
so here I will write X underscore train
comma X underscore test
then y underscore train
comma y underscore test equals to
train underscore test underscore split
then X comma y
comma test
let's go size equals to
0.25
okay press enter
so now let's convert text to vectors
for that I have to write here
that it's X
so here I will write from sqlarn
Dot feature
extraction
Dot text import
t
vectorizer
okay
then vectorization
equals to tfid
factorizer
okay
then
three
underscore
train
equals to
vectorization
or resided the ion refactorization dot
fit
that transform
X underscore train
okay
then
XV underscore test equals to
factorization
condition
Dot transform
X underscore test
okay then press enter
[Music]
so now let's see our first model
logistic regression
so here I will write
from
sqln Dot
linear underscore model
okay import
logistic
regression
then a lot
goes to
logistic
regression
and have to write here LR Dot
wait
then XV Dot
not DOT so dot train
comma
x v underscore test
okay
press enter
dot XV dot train
okay here I have to write y train
and press enter
we work so here I will write prediction
underscore
linear regression
question
dot predict
XV underscore test
okay let's see the accuracy score
for that I have to write LR DOT score
then XV underscore test
comma y underscore test
okay
let's see the accuracy so here as you
can see accuracy is quite good 98
percent
now let's print
the classification
code
I underscore test comma
prediction of linear regression
okay
so this is you can see Precision score
then F1 is code then support value
accuracy
okay
so now we will do this same for the
decision free gradient boosting
classifier random Forest classifier okay
then we will do model testing then we
will predict the score
okay so now for the decision tree
classification so for that I have to
import from SK learn
Dot 3
import
decision
three
classifier
okay
then at the short form I will write here
I will copy it from here
then
okay
then I have to write the same as this so
I will copy it from here
and
let's change linear regression
to
season 3 classified
okay
then I will write here same
let's go DT
question
DT dot predict
XV underscore
test
e
still loading it's it will take time
okay
till then let me write here for the
accuracy
DT DOT score
V underscore test
comma y
let's wait okay
let's run
the accuracy
so as you can see accuracy is good than
this linear regression
okay logistic regression
okay so let me
show you deep let me predict
trend
okay
so this is the accuracy score this is
the all the report
yeah
now let's move for the gradient boosting
classifier
okay for that I have write from
sqlan
dot ensemble
port
gradient
boosting
classifier
pacifier
I will write here GB
equals to let me copy it from here
I will give here random
let's go state
equals to zero
wait wait wait wait so I will write here
GB Dot
fit
three underscore train
comma
y underscore train okay then press enter
here I will write predict
underscore GB
was who
GB Dot
wait sorry
Reddit
three
DOT test
dot dot underscore test
till then it's loading so I will write
here uh it's for this code then I will
add GB DOT score
that
three underscore test
comma
y underscore test
okay
so let's wait it is running this part
till then let me write for the
printing this
okay it's taking time
taking time still taking time
but if I will run this
it's not coming because of this
yeah it's done now so you can see the
accuracies
not good then
decision tree but yeah it is also good
99
.4 something okay so
now let's check for the last one random
Forest
first I will do
for the random for us we have to write
from sqlarn Dot
symbol
import
random
Forest
classifier
okay
and here I will write RF
equals to
right I will copy it from here
then
random
date
question
zero
then
RF Dot
fit
three underscore train
comma y underscore train
okay then press enter
and predict
underscore
RC
or F
equals to
RF dot predict
C underscore test
okay
till then I will write it still loading
it will take time so till then I will
write for the score score accuracy score
XV underscore test comma y underscore
test
okay
then I will write here till then print
classification
code
and Y underscore test
comma
it will take time little bit
so
it run the accuracy score is 99 it is
also good
so now I will write the code for the
model testing so I will get back to you
but after writing the code so
so I have made two functions one for the
output label and one for the manual
testing okay
so it will predict
the all the from the all models from the
repeat so it will predict
the the news is fake or not from all the
models okay
so for that
let me write
here news
also string
what
okay
then I will write here manual underscore
testing
so
here I will you can add any news from
the you can copy it from the Internet or
whatever from wherever you want
so I'm just copying from the internet
okay from the Google
the news
which is not fake okay I'm adding which
is not fake because I already know I
searched on Google
so I'm entering this
so just run it let's see what is showing
okay
string input object is not callable okay
let me check this first
a I have to give here Str only
yeah let's check
okay I have to add here again the Escape
yeah
manual testing is not defined
let me see manual testing
okay I have to edit something
it is just GB and it is just RF
in GPS is not defined okay okay
so what I have to do
I have to remove this
this
okay
everything seems sorted
now
as I said to you
I just copied this news from the
internet I already know the news is not
fake so it is showing not a fake news
okay so now what I will do I will copy
one fake news from the internet
and let's see it is detecting it or not
okay
so let me run this
and let me add the news for this
so
all the models are predicting right it
is a fake news
or you can add your own script like this
is the fake news okay
I hope you guys understand
till here
so I hope you guys must have understand
how to detect a fake news using machine
learning you can you can copy it any
news from the internet and you can check
it is fake or not okay or if your model
is predicting right or not so if you
have any queries you can ask in the
comment section below our team will
respond you as soon as possible okay
don't forget to check the coursing from
the inbox below and you can download
this data set from the description box
below
and if you want this full code
full code just comment for the same
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