1.
LINEAR REGRESSION AND
PIVOT CHART ON
TOP 100 SOCIAL MEDIA
PROFILE DATASET
NAME: KHUSHAL CHOUDHARY BUSINESS INTELLIGENCE
ROLL NO. 8606 TEACHER: NISHA YADAV
CLASS : TYIT D

2.
INTRODUCTION ON LINEAR REGRESSION
Linear regression analysis is used to predict the value of a variable based on the value of another
variable.
The variable you want to predict is called the dependent variable. The variable you are using to
predict the
other variable's value is called the independent variable.
This form of analysis estimates the coefficients of the linear equation, involving one or more
independent
variables that best predict the value of the dependent variable. Linear regression fits a straight
line or
surface that minimizes the discrepancies between predicted and actual output values. There are
simple
linear regression calculators that use a “least squares” method to discover the best-fit line for a
set of paired
data. You then estimate the value of X (dependent variable) from Y (independent variable).

3.
INTRODUCTION ON PIVOT CHART
You can use a Pivot Table to summarize, analyze, explore, and present summary data.
PivotCharts complement PivotTables by adding visualizations to the summary data in a
PivotTable, and allow you to easily see comparisons, patterns, and trends. Both
PivotTables and PivotCharts enable you to make informed decisions about critical data
in
your enterprise. You can also connect to external data sources such as SQL Server
tables,
SQL Server Analysis Services cubes, Azure Marketplace, Office Data Connection (.odc)
files, XML files, Access databases, and text files to create PivotTables, or use existing
Pivot Tables to create new tables.

4.
DATASET ON TOP 100 SOCIAL MEDIA PROFILE
https://www.kaggle.com/datasets/medaxone/top-100-social-media-
profiles?resource=download
About Dataset
This dataset contains LinkedIn Influencers' post details and other
details(post dependent as well as independent) per post. This dataset can be
used to analyze LinkedIn reach based on post content and related account
details.
This dataset is great for Exploratory Data Analysis and NLP tasks.
The data was scraped using BeautifulSoup and Selenium.Last updated on
15th Feb,2021.

5.
LINEAR REGRESSION
specifying on comments and reactions
# x- reactions and y- comments
A) Create relationship model and get the coefficient
> )>x<-
c(11,15,44,22,21,16,40,29,14,12,7,12,20,8,12,24,23,19,7,7,3,1,2,4,5,8,3,2,2,17,5,8,3,1,3,1,0,4,0,
2,2,18,14,23,21,16,15,5,10,7,9,16,7,10,5,5,9,39,9,19,14,18,168,257,81,975,108,224,1239,424,716
,307,116,81,112,124,457,86,93,180,142,104,232,2,0,3,10,4,2,4,7,3,2,4,7,1,5,2,11,2)
> >y<-
c(0,0,0,2,1,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,1,3,0,0,0,2,0,0,0,1,0,1,0,1,0,1,0,1,0,2,4,0,2,0,1,
2,0,10,1,5,0,0,0,2,0,1,0,1,35,21,15,75,38,27,81,70,50,24,29,12,18,4,447,1,12,11,18,14,15,0,0,1,0
,1,4,2,2,0,3,28,1,6,4,0,1,19)
> relation<-lm(y~x)
> print(relation)

6.
B) Get the summary of relationship
> >x<-
c(11,15,44,22,21,16,40,29,14,12,7,12,20,8,12,24,23,19,7,7,3,1,2,4,5,8,3,2,2,17,5,8,3,1,3,
1,0,4,0,2,2,18,14,23,21,16,15,5,10,7,9,16,7,10,5,5,9,39,9,19,14,18,168,257,81,975,108,22
4,1239,424,716,307,116,81,112,124,457,86,93,180,142,104,232,2,0,3,10,4,2,4,7,3,2,4,7,1,
5,2,11,2)
> >y<-
c(0,0,0,2,1,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,1,3,0,0,0,2,0,0,0,1,0,1,0,1,0,1,0,1,0,2,4,
0,2,0,1,2,0,10,1,5,0,0,0,2,0,1,0,1,35,21,15,75,38,27,81,70,50,24,29,12,18,4,447,1,12,11,1
8,14,15,0,0,1,0,1,4,2,2,0,3,28,1,6,4,0,1,19)
> relation<-lm(y~x)
> print(summary(relation))

7.
C)Predict Function
x<-
c(11,15,44,22,21,16,40,29,14,12,7,12,20,8,12,24,23,19,7,7,3,1,2,4,5,8,3,2,2,17,5,8,3,1,3,1,0,4,0,2,2,18,14,2
3,21,16,15,5,10,7,9,16,7,10,5,5,9,39,9,19,14,18,168,257,81,975,108,224,1239,424,716,307,116,81,112,124,4
57,86,93,180,142,104,232,2,0,3,10,4,2,4,7,3,2,4,7,1,5,2,11,2)
> y<-
c(0,0,0,2,1,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,1,3,0,0,0,2,0,0,0,1,0,1,0,1,0,1,0,1,0,2,4,0,2,0,1,2,0,10,1,5,0,
0,0,2,0,1,0,1,35,21,15,75,38,27,81,70,50,24,29,12,18,4,447,1,12,11,18,14,15,0,0,1,0,1,4,2,2,0,3,28,1,6,4,0,1
,19)
> relation<-lm(y~x)
> a<-data.frame(x=170)
> result<-predict(relation,a)
> print(result)

8.
D) Visualize the regression graphically
>
> x<-
c(11,15,44,22,21,16,40,29,14,12,7,12,20,8,12,24,23,19,7,7,3,1,2,4,5,8,3,2,2,17,5,8,3,1,3,1,0,4,0,2,2,1
8,14,23,21,16,15,5,10,7,9,16,7,10,5,5,9,39,9,19,14,18,168,257,81,975,108,224,1239,424,716,307,116,8
1,112,124,457,86,93,180,142,104,232,2,0,3,10,4,2,4,7,3,2,4,7,1,5,2,11,2)
> y<-
c(0,0,0,2,1,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,1,3,0,0,0,2,0,0,0,1,0,1,0,1,0,1,0,1,0,2,4,0,2,0,1,2,0,1
0,1,5,0,0,0,2,0,1,0,1,35,21,15,75,38,27,81,70,50,24,29,12,18,4,447,1,12,11,18,14,15,0,0,1,0,1,4,2,2,0,
3,28,1,6,4,0,1,19)
> relation<-lm(y~x)
> png(file="linear_regression.png")
> plot(y,x,col="blue",main="COMMENTS & REACTIONS
Regression",abline(lm(x~y)),cex=1.3,pch=16,xlab="REACTIONS",ylab="COMMENTS")
> dev.off()
> plot(y,x,col="blue",main="COMMENTS & REACTIONS
Regression",abline(lm(x~y)),cex=1.3,pch=16,xlab="REACTIONS",ylab="COMMENTS")

9.
COMMENTS AND REACTION LINEAR REGRESSION

10.
PIVOT CHART
SPECIFYING NAMES AND THEIR FOLLOWERS

11.
PIVOT CHART
SPECIFYING NAMES AND THEIR COMMENTS

12.
Explanation
The following graph represents the relation between
the Reactions and comments
on each article of each influencers.
Here X has the value of reactions and Y has the
value of comments. We use linear regression to
predict Y and X.

THANK YOU