Indian car buyer's behaviour- using SAS VISUAL ANALYTICS

Here is an interesting insight of  car buyer's pattern.
A sample of 99 cars is analysed. 13 variables. Have a look of the data audit done using SPSS Modeler.

Now comes the slicing and dicing part.

We can observe that the a post graduate whose wife is working has a more chance of buying a car. 

secondly compared to the education level post graduates bought the higher percentage of cars.

The box plot below gives an idea about the salary range, mean , median, mode, make of the car and its count. An example of good visualisation in one graph.

Does Home loan has an impact ? find the answer below...

What is the impact of count when it comes to home loan ?

Thanks Ramprsath for support and technicals

Analysing the employment data from Indian railways

Source data from GOI
Credits: Ramprasath

Here is an interesting data to analyse.

How was the employment generated by Indian railways in the past 15 years ? Here is the insights.
The code was developed in R
Data preparation:
The rows and column are transposed to make it easy and readable.

setwd('d:/suman')
job<-read.csv('railwayjob.csv')
typeof(job)

for (j in 2:15)
{
  maxval<-max(job[2:19,j],na.rm = TRUE)
  minval<-min(job[2:19,j],na.rm = TRUE)
  y<-which(maxval==job[,j])
  z<-which(minval==job[,j])
  ans<-job[y,1]
  ans2<-job[z,1]
  print(paste("sl = ",j,", min = ",ans2))
  print(paste("sl = ",j,", max = ",ans))

}

This is a simple data set  for a period of  15 years  and  20 rows.Each indicate the railway zone.

The results are interesting:
Northern zone are contributing more for the employment generation.
The least were the kolkata.  More visualization can be added to this.