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.

Below is the Star Schema in regards to Credit Risk Excel Sheet. The fact table contains a primary key of Loan ID as well as Checking and Savings. The foreign keys are all pulled from the primary keys in the dimensions. Most of these dimensions were all categorical records, so by placing them in tables as dimensions, one could assign them as numbers rather than text, making it easier to read and work with the data. Also, I created a User table, in order to keep track of Months Customer and Months Employed, and then the age and gender characteristics (leaving Gender categorized as M or F). I then assigned User ID the foreign key of Job ID to help with the relation of the “Months employed.”

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.

#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
#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)
spreadChecking
## [1] 3147.183
#Find the standard deviation of savings 
spreadSavings = sd(savings)
spreadSavings
## [1] 3597.285

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
#Find the snr of the savings and name it snr_Saving
snr_Saving = meanSavings/spreadSavings
#Call snr_Saving
snr_Saving
## [1] 0.5038695

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

Savings has a higher SNR because the ratio of the average in respect to its standard deviation is higher than this calculation for Checking. At a first glance you would think that Savings would have the lower Signal to Noise Ratio because it has a spread (range) larger than that of Checking, but when performing the calculation, it is the other way around.

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.

I thought the above graph best represented the trend of “Months Employeed over Age by Gender.” It is clear here how and when Age and Gender played a role in how many months that person was employed. Overall, one can tell from this graph that males were employed more months than women most of the time.

This bar graph above also shows the relationship between age and gender in respect to months employed. It is very clear here how many months a male of a certain age was employed compared to number of months a female of a certain age was employed. Again, one can see that overall males worked more months than women, however at ages 57 and 58 (in this data set), it seems that females worked more months than men (men at that age are actually not present in the data set).