Now, we have imported data and our data is ready.
So let's run data on this data.
Duran unity.
Just straight your variable name.
That is beef don't describe.
This is our unity.
So let's go through all the variables.
When wavin.
As we calculated using the ship, the total number of records are 506.
So for price, very well.
Our total record is 506.
And the mean price, house prices, twenty two point five to.
We also have a standard deviation of this video.
But the minimum value is five and the maximum value is 50.
And the median is 21.
Median is always represented as the four feet percentile value.
On this information, you can see for all of the variables.
There are two important values that we should look for, and then UDD first is the common variable.
So on for almost all the variables, is 506.
But for and horse backs.
That is the number of hospital beds, but posehn.
The number is not five zero six.
This means that there are eight missing values in our data for this video.
But.
We will treat this missing values and the later part of the video, but at the moment we only want to
look at what all the variables have a missing value in them.
So it looks like on the end, Horsburgh missing.
They lose.
The second important thing, boom or bust, is the difference between the mean and median.
If you see there is no significant difference and mean and median off price, there is a significant
difference in the mean, then maybe enough time rate.
You can also look at them.
You, Maxwell, 25 percent dead and the 75.
But son died of that variable.
You get a sense of the distribution of data of that variable.
If you see the minimum value is zero point zero zero six and the 25 percentile value is zero point zero
is eight.
This means that there is little difference between the zero percent and the minimum value and the 25
percentile value.
And there is a large difference between the 75 percentile value and the max value.
This means that a word data for this variable is negatively skewed.
Most of the values lies in the small range of zero to three point six.
And the rest of values are dispersed from three to 88.
This means that our data is skewed for this variable.
If you look at the mean and median value of other variables, you will find out that for.
And hotel rooms, the maximum value is significantly larger than the sum in the fifth.
The mean value.
The largest value is one zero one.
Where does this end?
Seventy fifth value is 14.
And defended by Lewis on the 11:00.
So data may be positively skewed or there may be some outliers in this data.
Similarly for rainfall, also, we can see that.
The minimum wage, Lewis three, then the five percentile, Lewis, 28, and them and then the maximum
value is 60.
So here I can see that the data is either negatively skewed or there is an outlier in this data.
We'll discuss about outliers and missing value.
And then what?
Next videos.
But at this point, we will use a duty display, identifiers, such variables.
As you can see, we've identified this issues in our data using duty.
And you should always look for a duty before starting your analysis.
Since we identified problems with the word rainfall and added hotel room data.
Will not scatterplot for this data to understand them completely.
To Blätter Scatterplot.
I will use as.
That is Seabourne function joint block.
As soon as don't join Lord.
The expendible.
It's my end or Delarue.
Remember to put variable names in quotes.
And then y equal to.
Twice since my dependent variable is price and put by equal to price, and then they take away to be.
Remember?
Right, we're able limbs that at least.
You can see on the top we have the highest program of any hotel rooms.
And on the right, we have his program of price.
And in between.
In between the main plot, we have the scatter plot of Christ versus and hotel rooms.
One thing you can quickly identify from this graph is the two outliers.
These two points are at very large distance from the rest of the points.
So most of my data lies between zero and twenty four and hotel rooms.
But there are two points for which we have a value near to a, B and hundred.
Similarly, will Blätter scatterplot for rainfall also right?
Asanas don't join plot.
You will, right?
Exequatur rainfall.
And by equal to price.
And there details, details, Deth.
Now, again, on the top, we have a stroke rahmah of rainfall.
And on the right, we have a stroke Raam of Y.
And in between we have a distribution or scatterplot of rainfall towards this place.
Here you can see my rainfall values lies between 20 and 60.
Most of my rainfall values lies between 20 and 16, but I can see one outlier.
There is a single point for which the value is almost like four or five.
This may be due to the some sampling error or calculation error or something else.
And we'll learn more about outliers and our outlier videos.
Now, we have only covered numerical variables.
Now let's move on to categorical variables.
So, again, we will take a sample of five values, will, right, B.F. Dopehead.
And we tried to identify our categorical variables or first psychological variable variables airport.
To analyze this will lower the bar.
Lord Aurecon, Lord of Categorical, everyone will read as an assault on Lord and then exequatur to
our categorical variable, which is airport.
And then they take what it be, a bull run this now we have a lot of work, ecological way.
Well, airport and you can see their distribution on this airport.
There is nothing unusual in this data.
So we'll move on to the next set of what he calls medieval.
Is, though, what everybody.
In Wheelwright, that's an assault on blood mommai exists waterboarding.
The days of.
Again, you can see there this region of waterboardings.
And there is nothing unusual about that also.
So we'll move on to the next variable.
There is a bus terminal.
Yet you can see my bus terminal is only taking one value that this.
Yes.
So this may not be useful in our data, since this will not brought any differentiating power for our
dependent variable.
And for all the values of dependent variables.
This will be us.
So and all the cases, this will not impact our model anyway.
So we'll look at it in more detail in the later part of this court's.
For now, we will just mark this variable and we'll write some of the observation that we have from
looking at the duty and programs and Bartletts, our first observation is about the missing values in
very well and also Broome's.
Second observation was Skewness or all players in the variable, the crime rate.
Or total obliteration was outliers and.
Hotel rooms vary, but that is an outcomes.
And also in the variables, rainfall.
I our fourth observation.
Was about the bus terminal where even it was only taking one value.
We'll look at this observations in the later part of the sports.