All in all, our position, we have gone through with lots of analysis from importing the data from
database, and at times we have performed some sentiment analysis with respect to positive, with respect
to negative lots of things, this word cloud and not something.
And this is your what are my top 10 users to which Amazon is going to recommend product.
So session the very first part of the statement is we have to analyze the length of the comment, whatever
the user are going to give.
So we have to analyze whether they are lendee over that short one.
Or you can say you can analyze its distribution as well.
So maybe some of you have not that much good specification in terms of the processor or hard disk.
So you're according to your system specifications, you can consider some sample of this huge chunk
of data because you will figure out this is very huge data so you can consider some sample of data.
And yeah, if you don't have some issue with the specification, just go with this bulky data.
It's all up to you literally.
It's all about you say for your sake, I'm going to consider some sample of data.
So here I'm going to say either you can consider some sample of data using this data sample what our
sample size you want to assign using this, you can get some sample or you guys can consider some sample
as legacy.
And when you consider some top two thousand rows, it's all up to you.
Let's say this is my final little frame, just executed now is on this final.
I'm going to call ahead.
So this is that data frame on which I had to perform this analysis.
Check whether I have any missing value in this data or not for this.
I'm just trying to say is and a dot some are you guys can also use this is not awesome.
It's all up to you.
And on this, I'm just going to call this Willetton.
Maiya, I don't have any missing value in my data.
Now, let me check whether if I have any duplicates in or not.
So for this, I'm just going to say final dot.
I have a function which is exactly duplicated, dark, some just executed.
Now it will return me.
Yeah.
You don't have any duplicates in your data.
Now what you have to do, you have to analyze the land of the customers comment.
So now what I am going to do, I'm just going to say in this text column, I have to extract what exactly
the length of each of the feedback given by my customer.
So here I am going to say, let's say final of text.
Very first.
I have to access this.
Let's say I'm just going to access my zeroth index data.
Just paint it.
You will see it has that much number of words in this feedback.
Let's say I have to split it.
Let's say I have to split it on the basis of, let's say, space separated, just executed.
It will be done with this list.
And if on this list I'm going to calculate the length of this entire list.
Now, you will figure out this combined has that much land in a similar way.
You can compute the length of each and every comment.
So for this, you guys can define a function to do this operation in a much more user friendly way.
So here I'm going to say calculate land and whatever text I'm going to pass to my function, it will
exactly return me my land.
So here I am going to say whatever text I have on this.
Very first I have to close on the split and I have to split it on the basis of the separator.
Once I have all this stuff that I'm going to compute land the simple that just like a piece of OK,
now whatever land you have, you have to just return it.
That's it.
That's a task of the function.
Now what you have do you have to map dysfunction or you can say you have to apply this function on your
text column.
So final text dot apply and here you have to say calc underscore, just press tab.
So it is exactly calcaneus colon.
Now whatever land it will hit on me, I'm going to store it somewhere else.
Let's say I define a column for that.
So I'm going to say it is nothing but text, underscore and whatever name you want to assign, just
execute it and it will give me some warning.
Just don't worry at all.
It is just because of some your anaconda's choose.
Some of you have some personal issues, library issues.
It is just because of so many issues.
Just ignore this.
Wanting to know what you have to do.
You have to basically let's say I just need.
Distribution of this tax land so far, this you guys can use a very handy function, which is exactly
my box plot, so very close I have to import my Lochley because I want to use that box dot, which is
exactly in my plot module.
You can also use box stored in your Poinar Seabourne and many other things, but I just need some user
friendly visuals.
That's one way to use it here.
So I'm going to say Pratley Dot Xpress as a bit and if you haven't told us you can install using this
PIP install, pip install what the US can install using this basic command.
Now what I have to do, I have two very first imported.
Now using this I have a function which is exactly box just passive gustad.
You have all these different different parameters Saussure.
Now the very first parameter is exactly what is the frame which is final.
And on this Y-axis, I'm just going to say I have to pass this next underscore land just executed.
It will take some couple of seconds and it will return as my beautiful box brought in a while.
So this is exactly that box, part of which I'm talking about.
So if you have to conclude from this box plot, you will fill it out.
It seems to have almost 50 percent users are going to give their feedback almost almost.
You will figure it out, almost 50 words where there are only few users.
You will see there are a few users who are going to give a word Landi feedback's, because these DOTD
are exactly my outliers over here.
So that's a conclusion that I have derived from here, from my data.
So let's go ahead with our next thought on a statement in which I have to analyze our score.
So in this final data frame here, you will figure it out.
Here you have a column name of the score.
So you have to analyze this feature for this.
What I'm going to do, I'm just going to call my control over here.
So as dot com plot and here I'm just going to say final score just executed.
It will return this beautiful plot.
So from this, you guys can figure out most of the customers are going to give us five score on any
of the product.
So that's the type of conclusion you can fetch from all the visas that you have over here.
But believe me, 70 to 80 percent of the time is still gas is stand in your data preprocessing and in
your data.
That's what I had learned from my own experience working in analytics domain and in data science industry.
So I hope you will love this session very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.