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Weka Tutorial 02 Data Preprocessing 101 (Data Preprocessing) Video Lecture
Text Transcript from Video
hello everyone welcome to the second
tutorial of wake up workbench for
machine learning and data mining from
this tutorial on we will see how we can
use Weka in order to mine data or in
order to analyze your data for machine
learning or data mining activities so
there are several advantages of using
wake up in these days because waker has
four applications that are very
important in machine learning
that's explore our experimental
knowledge flow and simple CLI so
whenever you run or fire rika then you
will be able to have a GUI chooser just
like this one and from the GUI chooser
you can you can select any any one of up
these four applications so for this
moment we will stick with explore and
explore some of the features of Explorer
and we will see what we can do and what
we can do with those features of this
application Explorer of wake up so one
key important thing about choosing Weka
Explorer is that when you are having
your data set size small to medium this
is we're going to be very useful to the
Explorer to analyze your data but the
thing is that if for it explore the way
it works is that whenever you put data
to analyze for Explorer that time
Explorer uploads or takes all the data
into its main memory into the main
memory of your computer so if you are
having huge number of data and large
number of data sets that time it's not a
good good idea to choose Explorer
because that time your application may
get run make it run slower slower end
and it may crash and sometimes so this
is a very good tool to analyze a small
to medium size data so if you choose the
Explorer you will be able to see a
graphical windows just like this one and
this is a very helpful tool and it has
lots of tabs you can see here
pre-process classify cluster associated
select attributes visualize and so on
but for this tutorial we are going to
explore what we can play with
pre-process tab so in our previous
tutorial tutorial we have learned how to
create an ARFF
that's the that's the main file that's a
file Rekha knows and when you are going
to put your own put all of your data for
analyzes so this is very helpful that
wake up for with the wake up package we
you can access lots of our file
predefined this is predefined by the
altars of wake up so in order to find
those files you have to open 5:50 you
have to click the Open File button and
here you have to go to the place where
you have installed wake up its program
files for me okay sorry sorry about that
because we are searching for some our
files that's in wake up 2.6 that's my
version you yours can be others other
version so inside wake a you can see a
directory called data and if you click
data and you can see lots of our files
predefined for you for your playing and
they are given to you as a gift from
waking alters so we were playing with
the weather darf in our previous example
so we are going to stick with that one
so when you load these data so you have
to remember that when you are playing
with explorer of wake up that time
whenever you are loading a data file or
our file those data are going to
straight are strictly going to the main
memory of your computer so if you have
large number of data from large number
of data sets that time this is not a
good idea to choose explore anyway so
you can see that we have some
information about the current relation
the relation name is weather instances
is 14 that means that in your arm file
there are 14 data or 14 instances of
data and 5 attributes that means that
the file contains 5 features and those
features are listed here
the first one is outlook second one is
temperature third humidity for 20 and 50
s play so whenever you select outlook a
feature here you can see that this in
pain is showing you some
formation about the selected attribute
so in if you if you carefully examine
that actual are file you can see that
there is no missing values ok what's the
missing value sometimes when you are
pre-processing or processing your data
and converting those data into our file
this is very possible that you're going
to miss some of those values so whenever
you are missing some of those values you
just do not put null like in SQL
you just put a question mark so that
means that value is missing so if you
have 10 values missing from 10000 of
data that's not going to be affecting
that much your machine learning process
so there is no missing value for this
particular feature and this is a nominal
type of feature that means it doesn't
take any kind of numeric values and you
can recall from our previous tutorial
that outlook actually has three values
sunny overcast and rainy and they are
listed here you can see sunny overcast
and rainy and among those 14 instances
five are sunny four are overcast and
five are raining so this is going to be
very useful for you if you want to
analyze your data statistically okay so
the distinct three means that there are
three distinct values sunny overcast and
rainy and it automatically finds out
what what's the number of distinct
values and for this particular feature
if you if you select temperature and you
can see that temperature is not actually
nominal type of feature that's a numeric
type of feature and among them trill
what this thing that means two of them
are two of those temperature data inside
your weather dot are file they're the
same there with the same value so there
is no missing value for this particular
feature and ten is ten among them are
unique that means they do not match with
each other so among those of features at
fourteen features the minimum number is
sixty-four maximum is 85 and this is for
this is to boost your statistical
analyzes that they also provided a mean
and a standard deviation for any
particular
numeric type of feature not for nominal
features you can see that min is seventy
three point five seven one so it adds
soft it is sub 8's all the temperature
values on your art file and then divided
into divided it by the number of
instances and it gets the mean and so as
the standard deviation humidity this is
also the America type of features so I'm
not going to explain that again because
it's similar to temperature windy this
is not on numeric this is nominal type
of feature and we have two distinct
values here one is true and one the
other is false so there are six
instances that says that it is windy and
eight instances that say it's not windy
and if you choose a Play feature this
play feature is actually the
classification feature it's either yes
or no so we have years file nine times
in your our file and we have no five
times in your files and we have two
distinct values so you can carefully
observe that what this classification
feature particularly playing is showing
us we can see that when you are hovering
your mouse here you can see that ninety
instances are blue and they are yes and
five instances are red so they are known
so if you go back to other features you
can see that five of them states that
sunny and most of them are red that
means that in your art file the data is
exactly in the same way that if it is
sunny most of the times you're not
allowed to play outside and if it is
overcast outside then all of the
instances are saying that you you are
eligible to play outside on the other
hand for rainy we have mostly you can
play outside if it is raining outside
you can see the red portion is much more
smaller than the blue portion by this
you can say what's exactly happening in
your data so as for temperature we have
64
up to 85 and you can see and that
whenever the temperature is a is getting
greater and greater that time you are
allowed to play outside but when it's
very cold outside you are not allowed to
play outside same for humidity it is if
it is not that much humid then you are
allowed to play outside and if it is
getting very humid a we're going to be
sticky when you're playing outside so
it's forbidding you to play outside okay
so uh I mentioned one thing it's okay I
think okay for windy feature you can see
that six of them are true if it is
really windy most of the times well it's
a 50-50 chance if it is windy then you
can play or you cannot play but if it is
not windy there is the most of the times
you were allowed to play outside and the
red portion is very smaller than the
blue portion so that's it for this
tutorial so in this tutorial we have
learned how to use the Explorers
pre-process tab there are some other
things here open file we have already
seen that open URL open DB generate edit
save and this is the most important
thing filter we will talk about filter
in some later time in any other tutorial
but in this tutorial we have seen how to
open a file open an our file how to see
the number of attributes instances and
attributes and what we can do actually
what we do get from these features and
their information so that's it for today
tutorial of wake up workbench for
machine learning and data mining from
this tutorial on we will see how we can
use Weka in order to mine data or in
order to analyze your data for machine
learning or data mining activities so
there are several advantages of using
wake up in these days because waker has
four applications that are very
important in machine learning
that's explore our experimental
knowledge flow and simple CLI so
whenever you run or fire rika then you
will be able to have a GUI chooser just
like this one and from the GUI chooser
you can you can select any any one of up
these four applications so for this
moment we will stick with explore and
explore some of the features of Explorer
and we will see what we can do and what
we can do with those features of this
application Explorer of wake up so one
key important thing about choosing Weka
Explorer is that when you are having
your data set size small to medium this
is we're going to be very useful to the
Explorer to analyze your data but the
thing is that if for it explore the way
it works is that whenever you put data
to analyze for Explorer that time
Explorer uploads or takes all the data
into its main memory into the main
memory of your computer so if you are
having huge number of data and large
number of data sets that time it's not a
good good idea to choose Explorer
because that time your application may
get run make it run slower slower end
and it may crash and sometimes so this
is a very good tool to analyze a small
to medium size data so if you choose the
Explorer you will be able to see a
graphical windows just like this one and
this is a very helpful tool and it has
lots of tabs you can see here
pre-process classify cluster associated
select attributes visualize and so on
but for this tutorial we are going to
explore what we can play with
pre-process tab so in our previous
tutorial tutorial we have learned how to
create an ARFF
that's the that's the main file that's a
file Rekha knows and when you are going
to put your own put all of your data for
analyzes so this is very helpful that
wake up for with the wake up package we
you can access lots of our file
predefined this is predefined by the
altars of wake up so in order to find
those files you have to open 5:50 you
have to click the Open File button and
here you have to go to the place where
you have installed wake up its program
files for me okay sorry sorry about that
because we are searching for some our
files that's in wake up 2.6 that's my
version you yours can be others other
version so inside wake a you can see a
directory called data and if you click
data and you can see lots of our files
predefined for you for your playing and
they are given to you as a gift from
waking alters so we were playing with
the weather darf in our previous example
so we are going to stick with that one
so when you load these data so you have
to remember that when you are playing
with explorer of wake up that time
whenever you are loading a data file or
our file those data are going to
straight are strictly going to the main
memory of your computer so if you have
large number of data from large number
of data sets that time this is not a
good idea to choose explore anyway so
you can see that we have some
information about the current relation
the relation name is weather instances
is 14 that means that in your arm file
there are 14 data or 14 instances of
data and 5 attributes that means that
the file contains 5 features and those
features are listed here
the first one is outlook second one is
temperature third humidity for 20 and 50
s play so whenever you select outlook a
feature here you can see that this in
pain is showing you some
formation about the selected attribute
so in if you if you carefully examine
that actual are file you can see that
there is no missing values ok what's the
missing value sometimes when you are
pre-processing or processing your data
and converting those data into our file
this is very possible that you're going
to miss some of those values so whenever
you are missing some of those values you
just do not put null like in SQL
you just put a question mark so that
means that value is missing so if you
have 10 values missing from 10000 of
data that's not going to be affecting
that much your machine learning process
so there is no missing value for this
particular feature and this is a nominal
type of feature that means it doesn't
take any kind of numeric values and you
can recall from our previous tutorial
that outlook actually has three values
sunny overcast and rainy and they are
listed here you can see sunny overcast
and rainy and among those 14 instances
five are sunny four are overcast and
five are raining so this is going to be
very useful for you if you want to
analyze your data statistically okay so
the distinct three means that there are
three distinct values sunny overcast and
rainy and it automatically finds out
what what's the number of distinct
values and for this particular feature
if you if you select temperature and you
can see that temperature is not actually
nominal type of feature that's a numeric
type of feature and among them trill
what this thing that means two of them
are two of those temperature data inside
your weather dot are file they're the
same there with the same value so there
is no missing value for this particular
feature and ten is ten among them are
unique that means they do not match with
each other so among those of features at
fourteen features the minimum number is
sixty-four maximum is 85 and this is for
this is to boost your statistical
analyzes that they also provided a mean
and a standard deviation for any
particular
numeric type of feature not for nominal
features you can see that min is seventy
three point five seven one so it adds
soft it is sub 8's all the temperature
values on your art file and then divided
into divided it by the number of
instances and it gets the mean and so as
the standard deviation humidity this is
also the America type of features so I'm
not going to explain that again because
it's similar to temperature windy this
is not on numeric this is nominal type
of feature and we have two distinct
values here one is true and one the
other is false so there are six
instances that says that it is windy and
eight instances that say it's not windy
and if you choose a Play feature this
play feature is actually the
classification feature it's either yes
or no so we have years file nine times
in your our file and we have no five
times in your files and we have two
distinct values so you can carefully
observe that what this classification
feature particularly playing is showing
us we can see that when you are hovering
your mouse here you can see that ninety
instances are blue and they are yes and
five instances are red so they are known
so if you go back to other features you
can see that five of them states that
sunny and most of them are red that
means that in your art file the data is
exactly in the same way that if it is
sunny most of the times you're not
allowed to play outside and if it is
overcast outside then all of the
instances are saying that you you are
eligible to play outside on the other
hand for rainy we have mostly you can
play outside if it is raining outside
you can see the red portion is much more
smaller than the blue portion by this
you can say what's exactly happening in
your data so as for temperature we have
64
up to 85 and you can see and that
whenever the temperature is a is getting
greater and greater that time you are
allowed to play outside but when it's
very cold outside you are not allowed to
play outside same for humidity it is if
it is not that much humid then you are
allowed to play outside and if it is
getting very humid a we're going to be
sticky when you're playing outside so
it's forbidding you to play outside okay
so uh I mentioned one thing it's okay I
think okay for windy feature you can see
that six of them are true if it is
really windy most of the times well it's
a 50-50 chance if it is windy then you
can play or you cannot play but if it is
not windy there is the most of the times
you were allowed to play outside and the
red portion is very smaller than the
blue portion so that's it for this
tutorial so in this tutorial we have
learned how to use the Explorers
pre-process tab there are some other
things here open file we have already
seen that open URL open DB generate edit
save and this is the most important
thing filter we will talk about filter
in some later time in any other tutorial
but in this tutorial we have seen how to
open a file open an our file how to see
the number of attributes instances and
attributes and what we can do actually
what we do get from these features and
their information so that's it for today
About this Video
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Apr 29, 2026 Last updated
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