Continuing from my previous post, in this post I will discuss on the inferential and predictive analysis.

**About the dataset and the problem to solve: a brief**

The dataset is derived from UCI Machine learning repository and the task is to predict if a donor has donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). There are 776 instances in 6 six variables and it is a classification problem.

A. **Correlation**

As a first measure, I check for strongly correlated predictors. The correlation between two variables is a number that indicates how closely their relationship follows a straight line. correlation refers to Pearson’s correlation coefficient. A correlation of 1, indicates perefct linear correlation. I notice that the predictor `total number of donations`

and `total blood donated in c.c`

are linearly correlated. There is a fairly strong negative linear association between number of donations and months since last donation (corr= -0.159). Next, to visualize the pairwise correlational matrix, I use the `pairs.panel()`

from the `library(psych) `

which is shown in Fig 1

library(psych)
pairs.panels(train.data[c("Months.since.Last.Donation","Number.of.Donations","Total.Volume.Donated..c.c..","Months.since.First.Donation","Made.Donation.in.March.2007")])

Fig 1: Correlational matrix

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