About

This worksheet includes three main tasks in data modeling (a key step to understand the data), basic steps to compute a simple signal-to-noise ratio, and data exploration to identify trends and patterns using Watson Analytics.

Setup

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Task 1

To begin the Lab, examine the content of the csv file ‘creditrisk.csv’ by opening the file in RStudio. You can use File -> Import Dataset for that purpose.

Create a simple star relational schema in ERDPlus standalone feature https://erdplus.com/#/standalone, take a screenshot of the image, and upload it below.

To add a picture, use the directions found in Lab00. Below are steps and an example to create a simple star relational schema in ERDPlus.

Steps to create an star relation schema using erdplus.
From the drop-down option select New Start Schema
Example of how to create an start schema using erdplus
Completed Star schema example

Finally export the diagram as an image.

My work

Task 2

Next, read the csv file into R Studio. It can be useful to name your data to create a shortcut to it. Here we will label the data, ‘mydata’. To see the top head data in the console, one can ‘call’ it using the function ‘head’ and referring to it by its given shortcut name.

mydata = read.csv(file="data/creditrisk.csv")
head(mydata)
##      Loan.Purpose Checking Savings Months.Customer Months.Employed Gender
## 1 Small Appliance        0     739              13              12      M
## 2       Furniture        0    1230              25               0      M
## 3         New Car        0     389              19             119      M
## 4       Furniture      638     347              13              14      M
## 5       Education      963    4754              40              45      M
## 6       Furniture     2827       0              11              13      M
##   Marital.Status Age Housing Years        Job Credit.Risk
## 1         Single  23     Own     3  Unskilled         Low
## 2       Divorced  32     Own     1    Skilled        High
## 3         Single  38     Own     4 Management        High
## 4         Single  36     Own     2  Unskilled        High
## 5         Single  31    Rent     3    Skilled         Low
## 6        Married  25     Own     1    Skilled         Low

To capture, or extract, the checking and savings columns and perform some analytics on them, we must first be able to extract the columns from the data separately. Using the ‘$’ sign following the label for the data extracts a specific column. For convenience, we relabel the extracted data.

Below, we have extracted the checking column.

#Extracting the Checking Column
checking = mydata$Checking 

#Calling the Checking Column to display top head values
head(checking)
## [1]    0    0    0  638  963 2827

Now, fill in the code to extract and call the savings column.

My work
#Extracting the Savings Column
Savings = mydata$Savings

#Calling the Savings Column
head(Savings)
## [1]  739 1230  389  347 4754    0

In order to calculate the mean, or the average by hand of the checkings columns, one can add each individual entry and divide by the total number or rows. This would take much time, but thankfully, R has a command for this.

We have done an example using the checkings column. Compute the same using the savings column.

#Using the 'mean' function on checking to calculate the checking average and naming the average 'meanChecking'
meanChecking = mean(checking)

#Calling the average
meanChecking
## [1] 1048.014
My Work
#Find the average of the savings column and name the average of the savings meanSavings
meanSavings = mean(Savings) 

#Call meanSavings
meanSavings
## [1] 1812.562

Next, compute the standard deviation or spread of both the checkings and savings columns.

#Computing the standard deviation of standard deviation
spreadChecking = sd(checking)
My Work
#Find the standard deviation of savings 
spreadSavings = sd(Savings)

Now, to compute the SNR, the signal to noise ratio, a formula is created because there is no built in function.

SNR is the mean, or average, divided by the spread.

#Compute the snr of Checking and name it snr_Checking
snr_Checking = meanChecking/spreadChecking

#Call snr_Checking
snr_Checking
## [1] 0.3330006
My work
#Find the snr of the savings and name it snr_Saving
snr_Savings = meanSavings/spreadSavings

#Call snr_Saving
snr_Savings
## [1] 0.5038695

Of the Checking and Savings, which has a higher SNR? Why do you think that is?

Savings has the higher snr. This could be beacuse Savings had an average of 1812.56, which was higher than the average of Checking which was 1048.01. The higher average means there may be more data. This implies that their is more noise. When there is a lot of data, there is a lot of noise.

Task 3

Login to Watson Analytics and upload the file creditrisk.csv to your account. Use Explore to find patterns in the data. Consider for example trend of ‘Months Employed over Age by Gender’. Save your work and upload any screenshot(s) here. Refer to Task 1 on how to upload a photo. For every uploaded screenshot share your observations on general data trends and data behavior. Any screenshot without observations will be dismissed.

My work

In this trend we can see, on avergae, that adults between the ages of 27 to 41 have a higher number of months employed. We can also see that this inference mostly refers to males. Females on the other hand have a higher amount of months employed between the ages of 24 to 32.

In this bar graph, we are looking at the values of months employed and how they compare by credit risk. We can see that the higher the months employed, the lower their credit risk.

And when we compare credit risk to checking, we see that the higher the value of the checking account, the lower the credit risk.

So we can see that people who have a higher number of months employed, and a higher checking account, tend to have a lower credit risk.