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.

Star Relation Schema

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)

#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
#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 a higher SNR, meaning that there is less noise in this data set compared to the Checking column. In other words, Savings contain more useful information than Checking. This might be because the Checking column contains a lot more zeros than the Savings columns. The greater number of zeros can be explained by the fact that a Checking account is constantly changing and therefore, it is harder to keep track of. Additionally, based on the data, more people seem to have money in their Savings account rather than the Checking account.

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.

PROVIDE INSIGHT AS CAPTION

The word cloud shows that the most repeated option for Loan Purpose is Small Appliance since it appears in a bigger size. In other words, one can deduct that this means the most common purpose for a person to ask for a loan was to purchase small appliances. Based on the size of the words again, one can then observe that buying a new car was the second most common loan purpose, followed by furniture, business, used car, education, repairs, etc.

The word cloud shows that the most repeated option for “Loan Purpose” is “Small Appliance” since it appears in a bigger size. In other words, one can deduct that this means the most common purpose for a person to ask for a loan was to purchase small appliances. Based on the size of the words again, one can then observe that buying a new car was the second most common loan purpose, followed by furniture, business, used car, education, repairs, etc.

Based on the chart for Months Employed over Age by Gender, the two trends show that men tend to be employed for a longer period of time than women. Men reach their peak amount of months during mid 20s and mid 30s until early 40s.The maximum sum of months employed for a men at the age of 35 is actually about 700 months. This is relatively high, especially compared to the maximum sum of months employed for a woman, which is around 250 months at the age of 32. This comparision further reinforces the idea that in general, men tend to work for longer periods. In addition, the chart shows that towards the beginning and the end of their lives, both men and women are either employed for shorter lengths or not employed at all. Another observation is that men tend to still be working at the age of 73, while women usually stop around the age of 65 (0 months employed).

Based on the chart for “Months Employed over Age by Gender,” the two trends show that men tend to be employed for a longer period of time than women. Men reach their peak amount of months during mid 20s and mid 30s until early 40s.The maximum sum of months employed for a men at the age of 35 is actually about 700 months. This is relatively high, especially compared to the maximum sum of months employed for a woman, which is around 250 months at the age of 32. This comparision further reinforces the idea that in general, men tend to work for longer periods. In addition, the chart shows that towards the beginning and the end of their lives, both men and women are either employed for shorter lengths or not employed at all. Another observation is that men tend to still be working at the age of 73, while women usually stop around the age of 65 (0 months employed).