And this video, we are going to learn how to import the data into our software and get it ready to
start building the model.
I have ordered and here you can post the video and copy it out.
I'll also be sharing the file of discord in the resource section.
You can download that file and you can copy paste that Acleda code from that fellow to.
So the first part is how to import the dataset.
To import the data, we use this lead CSB function.
So if your data is saved as a we even if it is an excellent fight, you can open it in Excel and save
it as a CSP file.
So when you have us, yes, we find that there's a comma separated values file.
You can import that file using the dawsey as we function.
Indeed, NTSB function.
You have to give the.
Part of that faith and the data in that faith will be imported into this variable, which I have named
as movie.
So when I ran that command felt this commanded then which assigned the data in that file to a movie
variable.
When I run view this very well, I can see that data.
This is the data that we discussed earlier in the columns.
We have a rebuilt.
The last column will be the collection column, which is a dependent variable, Audie.
Very well that we want to predict.
And we have all the 506 observations.
And this data.
You can also note that the headers that is the column names, but auto by the DGSE function.
And it has identified that DECL column header.
What DVD?
Willemse.
So by using these two lanes, you can import the data into your, ah, software.
The next step is before running any model or training, any model.
We need to prepare the data.
I'm showing you only one step of data processing.
This step is called missing value imputation, that is whenever the value of variable is missing.
That we have to fill.
It is a critical step because if a value is missing in the data, say the models cannot be trained,
that is, and will not be able to Dundy functions to train these models.
So it is a mandatory step.
That is why I'm showing it to you.
There are various other processing techniques.
That we have to do to get Dee Dee Dee Dee.
If you want to learn those techniques, also, I'll be sharing the link of how you can learn those techniques
and the description.
I'm not going to cover that part in this video.
So how to handle missing values?
First of all.
Will then this command to see are there any missing we're losing in any of the variables.
So we'll run somebody of data.
And this somebody we get minimum, maximum and the three quartile values.
Apart from this, whichever variable has some missing values, it will get an additional information
of enemies.
So the time taken variable as will end is.
So in this variable, there are two entities which were empty.
So if you open the Excel file, there will be quilters empty.
When we have imported that file into art, those cells are named as any.
We cannot have any in our dataset.
So we'll be changing the end is to some value.
The value we are going to assign these blank cells should be some harmless value, such as mean or median.
So we are going to find out the meaning of the well losing time taken very well.
And we are going to put that mean value into these blank cells to do that.
The is by running this line, we can replace the blank values with the of other values in that particular
variable.
So let me explain to you what this line is doing on the left part.
We are identifying those cells which are blank.
So movie dollar time taken takes you to that particular variable.
And within that variable, you want to find out those cells where.
The value is any.
So is any function is finding out those cells which are having value.
Any in this variable.
And we are putting the mean of time taken variable after removing any values.
So we had five, 506, six observations.
Twelve of them had any value.
So for the remaining 494 observations, we find out demean using this function and report that mean
value and to those values which had any.
Using this single lane, we'll be doing missing value imputation.
Once the missing values are replaced or are imputed, we can run our analysis, what better analysis?
We should take some other steps also, such as outlier treatment, variable transformation.
Looking at correlations between variables and so on.
We will not be covering all those steps here after missing value in prediction.
Our data is ready so that we can use it for training our and remodel.