Didn't know we have discussed about fighting different Bible libraries like no one does, and we have
also discussed about how we can modify the data.
Now, as we know all of these, we actually need to understand how we would prepare the data using the
tools like Mambi and find those.
So the first thing which we need to understand is the different types of variables.
So there could be three types of input variables.
One is categorical variable.
Next, a quantitative variable and the last one being order would be.
Categorical variables hold the values that can be organized into categories, and these are not numerical
in nature.
Some of the examples of categorical variables could be.
City and gender of a person, the next category of the input variable date is numerical.
These could be age or salani, something which is numerical in nature and they can perform and mathematical
operation on both of these are called numerical believes.
The third type is or the movie or the variable is with a natural order, it is the variable on top of
which we can apply a sequence.
So here we have these ordinal variable rating, which has values, good average grade.
But I think by greed and good, these values, although they look like categorical variables because
these are in textual form, but they actually have a sequence present in them, we know that grade would
be the best rating while followed by a good then average, then bad.
In the end they would be pathetic.
Why, in case of Citi, we do not have any particular order.
We do not know how we can compare Delhi, Mumbai, Jenny and Golgotha.
So that is the reason why Citi is a categorical variable while reading is an ordinary gender again,
is a categorical.
Now, let us see how we can handle the.
So to the handling data, we have to think about the numerical.
So let us see, we have this kind of data.
Now, here we have each uncivility bhajan numerical in nature.
So we do not need to perform any operation on top of these.
The next is expedient, so although the values are numerical in nature, but they have some fixed presence
in them, so if you think of it, these will be present in a string.
So we will have to convert the expedients into a numerical form.
So we will have to remove the wire for you from this one year so that only one is remaining in a numerical
form.
Next next, we will have to remove this plus sign on air from zero plus here, we can either write zero
in place of zero years or we can write one wherever we have zero plus years.
Next is categorical data in case of categorical data, we need to convert these to numerical one, not
including legacy, how we can do that?
City is a is a categorical leader and so is Gendell, let us see how we can handle this.
So here we have the city here.
We have Daily Mail by Jenny Callcott.
This is the city column, which we originally had.
So when we want to convert this into a numerical form, what we can do is we can instead of having one
column for city, we can have four columns for the cities as we have four cities present in the city
column.
So now we have these four columns, Delhi, Mumbai, Chennai, and then the next thing what we can do
is we can put values inside this.
Now, let us try to fill this particular time.
Now, what I have done is inseparable over the city was daily in the daily column, I have put one and
in all of the places I have put to.
This week.
This way, all the columns will have value zero and one.
Which is easily interpretable by the machine.
Now, what we can do is let us try to notice a pattern between these.
So wherever the CB is, Delhi, the number against is one, and all of those values are zero.
Whatever the city's monthly, the value against Mumbai is one.
And all of those values are against you.
And whatever the value is, generally the value for China is one.
And all of those values are zero.
Now for Calcutta.
Again, all the values are zero except for the value under the column.
Now, instead of having these four columns, I can only have these three columns in.
If I keep only these three columns.
Then I will be showing the same information.
What will happen is whenever the value for the lanterne is one 00, it will mean that we are referring
to the whenever the value is zero one zero.
We are referring to Mumbai whenever the value is zero zero one, we are referring to generally.
And whenever all the values are three, we are referring to Calcutta.
So there is no need to have a column named Call that this will in turn reduce the number of columns
which we are having, because the high number of columns also leads to complexity in the calculation
of more.
Villone.
So it is always suggested to have less number of columns and the moving the column, Golgotha is actually
saving us one column and also not hampering the information.
We still have the same amount of information in time, but we are just not having one extra call you.
Now, the next is categorical data, so let us see another city, so let us look at this particular
data.
So here we have a beat.
So underbite, we have the the month I leave.
Now, when we have this particular date, it might not show any particular information because Mushin
would not be would not be able to understand what Abeed stands for so we can convert a single date column
into three different columns being the month I leave and for each day month, and we can fill in the
values of the date.
So for this particular date, the day will be one for this particular day.
The day will be to hear.
The day will be five.
Here they will be eight.
And the month will again be 12 one seven three, and it will be twenty twenty two thousand to twenty
twenty and 1982.
So this is how we will be able to convert the date, which is a categorical column, and we are not
able to understand what the stand for about a month, year column will be able to express the information
more clearly.
Next is the ordinal columns, so whenever we have all the new columns, what we can do is.
We can convert these to numerical columns.
So let us try to convert this, so when we have uncategorically column, which is an ordinary column
actually, so we can convert these into a numeric form, and instead of having five columns here, we
can simply have one single column with showing the reading into a numerical.
With great is depicted by five, good is depicted by four, average is depicted by three, then by the
BITOU and pathetic by one.
Here we have another example of categorical variable where we have gender and the gender has two values,
male and female.
So instead of having a gender column, we can instead have a male column.
And wherever the value of male is one, it will refer to mean and whatever the value is zero.
It will refer to female.
Here, the numerical values, so here we are simply removing the year from these values and converting
them into integer or floating point value so that we get the value in in the in a numerical form.
Let us discuss about gladius problems with videos.
When they are working with different variables and different features, we need to make sure that the
features which we have, are you busy being a good amount of data?
And also they should not have any irrelevant information.
For example, let us say we have some irrelevant columns, like if we are talking about a loan approval
application.
Now, the application number is a completely irrelevant column in this context because the application
number will not meet.
We will not make any difference in approval or rejection of the application.
Next is categorical claims.
Now, let us say we have a category column which have a lot of categories.
So in this particular situation, when there are a lot of categories, we cannot really Convergys into
one, including.
So in this particular case, we can select either the top seven or top 10 categories or we can divide
the categories into subgroups.
So let us see.
We have a guy named city or State.
So what we can do is if we have 50 cities, then we can maybe choose the most influential Thorpes,
five out of seven cities, or we can group the cities into metropolitan cities and so on, so that we
have all three groups of cities which in which multiple city scanline.
Then the next thing is usof anomaly detection.
There could be certain outliers or there could be some kind of data which is not really important in
nature.
So what we can do is we can use anomaly detection or we can use outliers or detection using box plots
and get rid of the outliers instead of outliers.
We can impute certain values.
We can replace these values with the mean of the data or the median of the data or the mood of the data
in case it is a categorical.
In case there are any missing values in that particular case, we can add some other data.
So in case of missing values, if there are a few values which are missing, then we can take a mean
or median of the data and include those values in place of the missing values in case the missing values
are a huge number.
Let us see, 90 percent of our data is missing.
In that case, we can directly get rid of the column itself because imputing values will not be fruitful
for us because we would be creating false values in that situation.
Next is noise, then there is some noise that is modification of the original value, these noise can
actually look like a normal input data, but has some kind of fault in it and it is very hard to detect.
So to avoid noise, what we can do is.
We can make sure that the data from our sources is being pulled correctly.
That is the only solution which we can have formalized eviction.