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Hello and welcome to this art tutorial.

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So we did the main steps.

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We cleaned all the texts, all the reviews.

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We created our backwards model
and now we have to do one more thing,

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which is of course to build our machine
learning classification model.

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And we can do that because we have
all our independent variables

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in this sparse matrix DTM here builds
thanks to this function Documenter matrix.

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And besides, we applied a filter here
to remove the non frequent words.

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Well a few of them.

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But still this considerably reduced
the number of words in the matrix.

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So that's always good for our model
to run faster.

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All right. So now let's build the model.

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So what we'll do is go to our files to
get back to the part three classification.

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Because what we'll do
of course is take a classification model

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that we already built.

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And we will apply it on our text here.

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Because out of this text,
we managed to create a matrix of features

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containing the independent variables.

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And of course we have

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one dependent variable
which is the second column of our data

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set the liked column, which tells
if yes or no.

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The review is positive.

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So we have

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everything and therefore
we only need to take our model now.

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And therefore we go
two by three classification.

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And here
we can find all our classification models.

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All right so which one to pick.

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Which one to choose
for natural language processing.

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Well in general based on experience,
the most common classification models used

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for natural language processing or Naive

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Bayes decision tree or random forest.

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You also have the cart model, which is
another type of decision tree model,

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and you also have the maximum entropy
model, which is based on entropy as well.

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Like for decision trees.

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So these models work
very well for natural language processing.

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And therefore he will pick
one that is related to entropy.

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And that's the case for decision tree
classification model

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as well as our random forest
classification model.

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Because of course

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a random forest is a combination of trees
making the same predictions together.

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And keep in mind
that you can also use Naive Bayes,

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which is commonly used as well
for natural language processing.

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But here in this tutorial we will choose
random Forest classification.

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So let's go into this section
and here are all the files.

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You know the data set the classification
templates and our model in Python and R.

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So let's take the one in R.

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So I'm just clicking on the file. Here
we go.

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Model open here.

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So what do we need here.

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Well first of all let's notice
that you know when we are using the random

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forest classification model
we are starting with a data set

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which is a data frame.

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And that contains both the independent
variables and the dependent variable.

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So what do we have to do right now
is go back to our natural language

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processing file and create
exactly the same.

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That is, create a data

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set containing independent variables
and one dependent variable.

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And that will be
the input of this model here.

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Because you know
we have our data set here.

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And then we use our data
set in each of the code sections here.

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And then you know, we split the data
sets into a training set and a test set.

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And we train our machine learning
classification model on the training

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set here.

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So what we only have to do
is just to create this data

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set containing the independent variables
and the dependent variable.

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So that's very simple.

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We already have our independent variables.

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But the problem is that our independent
variables right now are in a matrix.

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Because you know this document term
matrix function returns a matrix.

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So DTM is a matrix right now.

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And as you remember in our classification

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models on R
well this data set is a dataframe.

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It's not a matrix.

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So we need to make sure that here
for the inputs of the model

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that we are going to apply on this
bag of words model that we just created

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in the previous tutorials.

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Well we need to make sure
that we have a dataframe,

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but that's actually very simple.

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We just need to take our matrix
and use the function

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as that data dot frame.

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And we will input our sparse matrix
DTM inside.

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And that will transform our DTM
sparse matrix into a data frame.

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So let's do this.

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And since you know
we are just going to copy paste

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our random forest classification

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here, well, since the input of this model
is basically this data set,

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well here we will use the same name
to create this data frame.

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And so we will call it data set.

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Data set equals.

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And then that's when we use the as dot
data dot frame.

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Here it is. That's the first one.

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Here we go.

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And so now we need to input the matrix.

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We want to transform into a data frame.

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And that's of course DTM.

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And just to make sure
we have the matrix type

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expected by this as dot data
frame function.

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Well we need to use here
the function as dot matrix

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and put DTM
as input of this as matrix function.

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Because you know this sparse
matrix DTM here is definitely a matrix,

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but it doesn't have the type expected
by this as the data frame function.

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And to make sure
we have the right matrix type,

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well we need to use this
add that matrix function.

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All right.

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And now let's be careful.

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We lost one parenthesis.

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So I'm just adding it.
All right. Now we're good.

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We are ready to transform our sparse
matrix of features into a data frame.

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So let's do it
I'm going to select this line and execute.

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All right.

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And now what's interesting to see
is that we have the real data set.

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You know with all the reviews and the rows
and all the words that we took

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from the corpus.

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And then we're filtered thanks to this
remove sparse terms function.

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Well, we can see the full data
set here with this 1000 rows

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and all these 691 columns,

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each one corresponding to a word
that comes from the reviews in the corpus.

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And that was not filtered
by the remove sparse terms function.

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All right.

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So here
you can have a look at this huge table.

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And we can clearly see here

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that this is a sparse matrix
because basically we can only see zeros.

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Well we have very few ones.

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We have one here one here.

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But all the rest is zeros.

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And so for example
if I take this one here,

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well this one belongs to the also column
and to the 23rd row.

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That is the 23rd review.

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And so this one here means that the word

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also appears in the review 23.

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All right. So that's the sparse matrix.

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And now you can really see what it is
with your own eyes.

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All right.

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So let's go back to our natural language
processing file.

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So we have our data set
which is now a data

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frame as we wanted but still incomplete.

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You know why.

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It's because the data set we start with in
this random forest classification model.

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And in general we say classification
models is a data frame.

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So we're good on that.

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But a data frame containing
both the independent variables

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and the dependent variable.

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So what we need to do right now

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is add the dependent variable
to this data frame data set.

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Because right now it only contains
the independent variables.

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All right.

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So you might remember how to add the
dependent variable column to a data set.

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That is a data frame.

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Remember we need to take our data set.

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Then add a dollar sign here.

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And then after this dollar sign
we can either take one of

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the existing column here
if we want to update the column,

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or create a new column
to add to this data set.

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And that's exactly what we want to do.

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We want to create a new column
to this data set.

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Well that's an existing column.

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That's the light column.

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But we created for this data set
because it is new column.

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And so we'll give to this column
the same name as the real dependent

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variable column. That is light.

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All right.

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So by doing this we are adding
this new column that we call liked

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and then equals.

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And then after this equal

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we need to specify what we want to add
in this new column.

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And what we want to add is nothing else
than the existing

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liked column of our data set.

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But be careful, because our data set
was just a data to this new data frame,

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and therefore we no longer have the data
set that we imported originally.

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So what we'll do is very simple.

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We'll just rename this data set
by adding an underscore and then original.

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Here we go.

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And we will select this line again
and execute.

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All right.
So now we have our original data set.

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And therefore we can have access
to the liked column of this

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original data set which is going to be
our dependent variable.

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So let's add this dependent variable
right now to our data set.

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And so to take this dependent variable
we need to take our data set original.

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Here it is

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because that's the original data set
containing the dependent variable liked.

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And so to take this dependent
variable vector

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we need to add a dollar sign here same.

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And then take the column we want
which is the liked column.

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All right. So that's good.

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By selecting this line and executing it

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we add the light dependent variable
vector column

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to our data set already
containing the independent variables

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that are all the filtered words
of our cleaned reviews in the corpus.

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All right.
So now we have everything we need.

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And we are ready to take our machine
learning classification model

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because we have our data
set that not only is a data frame,

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but also contains both the independent
variables and the dependent variable.

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So we have everything.

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What is expecting a random forest
classification model here?

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So what we only need to do here is take
everything from here and not from here.

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You know, because this section
is to import the data set.

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But we already have our data set
that is ready for classification model.

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So we just need to take everything
from here because this is where

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the data set starts to be processed.

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And so we take everything from here to

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here.

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And we can not take this
because this is to plot the results in 2D.

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That is two independent variables.

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And here since of course we have
a lot more than two independent variables.

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Well,
we cannot use this to plot the results,

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but we will definitely have a look
at the confusion matrix

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to see the number of correct predictions,

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as well as the number
of incorrect predictions,

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so that we can evaluate
the model performance.

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All right.

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So let's get back to our natural language
processing file.

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And we will paste our random forest
classification model right here.

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All right.

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So now we just need to modify

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a very few things
because everything is basically ready.

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But let's see what we can modify.

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Well first here in the section
that encodes the target feature as vector.

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Well of course we need to replace this
purchased dependent variable

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which was the dependent variable
in part three.

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Well we need to replace it with our new
dependent variable which is liked.

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All right.

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And same here we replace purchased by like

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all right good for this section.

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Then in the next section we split
the data sets into the training set

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and the test set.

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Well that's very important to do this.

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Unless you want to create a new review.

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But you know we will train our random
forest classification

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models on say for example, 800 reviews.

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And we will test the predictive power
of random forests on 200

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new reviews on which our random forest
classification model was not trained.

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And therefore these 200 reviews

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and the test set will be new reviews
for a random forest classification model.

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And so we will see how it manages
to predict

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whether each of these 200 reviews
is positive or negative.

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And then that's in the confusion matrix
that will see

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the number of correct predictions
and the number of incorrect

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predictions in this 200 new reviews.

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All right.
So that's what is done in this section.

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And since I just gave as an example
800 reviews to train the model

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and 200 reviews to test it,
well let's go with this choice of numbers.

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And so we need to change the split
ratio here to 0.8 because that's 80%.

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And we have 1000 reviews.

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So 80% of 1000 reviews is 800 reviews
to go to the training set,

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and therefore 200 reviews
to go to the test set.

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All right. So that's good.

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And of course, let's not forget to replace

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the purchased variable here
by our new dependent variable.

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That is light.

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All right.

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So I think we're good with this section.

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So now let's move on to the next one.

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The next one is about feature scaling.

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And so here
do we need to apply feature scaling.

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Well not really

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because we only have zeros and ones
in the sparse matrix of features.

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And therefore we don't have one

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independent variable
dominating another independent variable.

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So we don't need to apply feature scaling.

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So we will remove this section.

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All right.

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And so what about this one.

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Yes of course we keep this one
because this is the section where we build

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a random forest classification model
that will classify the reviews.

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And that's

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where we train the random forest
classification model on the training set.

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And therefore here
we need to change two things.

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First, the index here that you know
is the index of the dependent variable

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that we need to remove from x
because x is supposed

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to be the training set
without the dependent variable.

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So we need to remove it with the index
of our new dependent variable

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like it is not three but is 692.

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We can see that here very easily.

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So let's replace three by 692.

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All right. Good.

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And now the second thing that we need to
change is of course this purchased here

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that we still need to replace by light

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this way.

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And then
if we want we can train our random forest

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classification with more trees.

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Right now we have ten trees.

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So we will keep ten trees.

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That might be enough for our 1000 reviews,

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which is quite a small number of reviews,
and especially our 692

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words columns that we have
in our sparse matrix of features.

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Ten trees might be enough,
but of course, you're welcome

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to try more random forest
classification models with more trees.

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So we're good with this section.

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And now let's move on to the next one.

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The next one is about
predicting the test results.

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So making the predictions
on 200 new reviews

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that our model won't know anything about.

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And therefore for this new reviews,
our model is going to try to predict

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if those reviews are positive or negative
and therefore it will be very interesting

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to see if it's making some correct
predictions on new reviews.

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So right now it's the same.

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We have to replace this index here
that corresponds to the index

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of the dependent variable.

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And so we need to replace three by
of course 692.

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That's exactly the same as we did
for the training set here.

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And so now we're good for this section.

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We're finally getting to the last section
that is making the confusion matrix.

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That's the interesting section
that will tell us the number of correct

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predictions and the number of incorrect
prediction for these 200 new reviews.

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00:14:51,633 --> 00:14:52,966
So we will see that right now.

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But of course we need to replace this
three index that corresponds

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to the index of the dependent variable
still the same.

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And replace it by 692.

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All right.

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So now everything is good.

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We are ready to train our random forest
classification model

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on our 800 reviews of the training set.

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And then evaluate
the predictive power of our model

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on our 200 new reviews in the test set.

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So let's do it.

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00:15:18,133 --> 00:15:22,733
Since we already executed everything
up to here, what we need to do now

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00:15:22,733 --> 00:15:26,400
is just select everything
from here to the bottom.

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And now we're good.

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00:15:27,800 --> 00:15:32,066
We just need to press command or control
plus enter to execute to train the model

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and test it on the test set,

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and eventually have a look at the number
of correct predictions and the number

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of incorrect predictions
on 200 new reviews.

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00:15:40,366 --> 00:15:41,466
So let's do it.

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00:15:41,466 --> 00:15:43,366
I'm going to press
Command Plus Enter to execute.

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And here we go.

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00:15:46,166 --> 00:15:48,200
Everything worked properly. Great.

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00:15:48,200 --> 00:15:49,633
So let's have a look.

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00:15:49,633 --> 00:15:52,200
We will have a look at the confusion
matrix.

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00:15:52,200 --> 00:15:56,100
Of course by typing
here c m in the console.

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00:15:56,433 --> 00:15:59,100
Here we go. So let's see what we have.

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00:15:59,100 --> 00:16:04,400
We have 79 correct predictions
of negative reviews, 70 correct

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00:16:04,400 --> 00:16:10,100
predictions of positive reviews, 21
incorrect predictions of negative reviews,

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00:16:10,500 --> 00:16:13,800
and 30 incorrect
predictions of positive reviews.

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00:16:14,233 --> 00:16:16,133
All right, so that's actually not too bad.

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00:16:16,133 --> 00:16:19,366
You know, because we only had 800 reviews
to train the model.

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00:16:19,500 --> 00:16:21,733
That's not much
when you're working with text.

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00:16:21,733 --> 00:16:24,666
And therefore 30 plus 21 equals 51.

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00:16:24,666 --> 00:16:28,700
Incorrect prediction
is not bad out of 200 new reviews.

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00:16:29,033 --> 00:16:33,600
When you know that you train your
classification model on only 800 reviews.

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00:16:33,900 --> 00:16:36,200
And actually,
let's have a look at the accuracy.

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00:16:36,200 --> 00:16:41,066
The accuracy is the number
of correct predictions that is 79

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00:16:41,300 --> 00:16:45,266
plus 70 divided by the total number

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00:16:45,266 --> 00:16:48,600
of observations in the test set,
and that is 200.

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00:16:49,200 --> 00:16:51,133
So let's have a look at the accuracy.

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00:16:51,133 --> 00:16:52,900
Pressing enter here.

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00:16:52,900 --> 00:16:55,900
And the accuracy is 74.5%.

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00:16:56,333 --> 00:16:57,033
So again

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00:16:57,033 --> 00:17:01,500
that's not bad considering the fact that
we trained our model on only 800 reviews.

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00:17:01,733 --> 00:17:04,866
And you'll clearly see that
if you had a lot more reviews to train

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00:17:05,066 --> 00:17:08,800
your classification model,
you will get a much better accuracy.

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00:17:09,566 --> 00:17:12,033
All right, so that's the end of natural
language processing

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00:17:12,033 --> 00:17:15,433
and are congratulations
for having completed all this.

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00:17:15,433 --> 00:17:18,733
Creating the Bag of Words
model training and classification model.

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00:17:18,733 --> 00:17:20,066
And this data set.

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00:17:20,066 --> 00:17:23,133
But that's not the end of your natural
language processing journey.

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00:17:23,133 --> 00:17:26,933
Because right after this video
you'll get a little challenge.

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00:17:27,233 --> 00:17:29,133
So we'll let you find out about that.

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00:17:29,133 --> 00:17:31,100
And until then, enjoy machine learning.