NEC Lending Club Data Programming Worksheet


Nutrition Case Study
The main objective is to write a fully executed R-Markdown program performing regression prediction for the response variable using the best models found for kNN, Random Forest and XGBoost techniques predicting the response variable in the Nutrition case study. Make sure to describe the final hyperparameter settings of all algorithms that were used for comparison purposes.
You are required to clearly display and explain the models that were run for this task and their effect on the reduction of the Cost Function.
Points will be deducted in case you fail to explain the output. 
Please note that all code assignments must be submitted as a screenshot with a slice of your desktop showing the timestamp.
If the time and date are not visible, you will be graded 0.

1 attachmentsSlide 1 of 1attachment_1attachment_1

Unformatted Attachment Preview

Week 9: Lending Club
We will revisit the Lending Club data for this week’s assignment. The company has existed since 2007 and
have provided millions of personal loans since then. Lending Club announced IPO in December 2014, since
when the company came in the limelight for negative publicity. Lending club officials were accused of
taking aggressive risks by lending money to those with risky credit worthiness. You are asked to study this
phenomenon and determine if data provides clues of the authenticity of the claim that Lending Club behaved
You are given a single combined file of “approved” loans data from six years, which are supposedly the pre and
post periods of the controversy.
Step 1 (30 Points)
The first step is create two new columns as follows:
a) Comb_Risk_One: Create a binary column by combining categories A and B (Low Risk) into one
category and all the remaining categories in another (High Risk).
b) Comb_Risk_Two: Create a binary column by combining categories A, B and C (Low Risk) into one
category and all the remaining categories in another (High Risk).
Now, break the file into two files filtering out data for 2012, 13, and 14 in one file and 2015, 16 and 17 in
another file.
Step 2 (70 Points)
The primary objective is to use classification techniques learnt so far. Each loan is graded (A to G) based on the
risk, with A being least risky and G being the highest risk category. You are asked to predict Low and High-risk
categories (for the two new response variables) using various modeling techniques like Naïve Bayes’, KNN,
Logistic Regression, and CART model. Make sure to look for the following:
Instructor: Prashant Mittal.
a. Outliers based on the independent columns (predictors)
b. Multicollinearity
c. Scaling and standardization of the predictors
d. Train-Test split for both files and compare the confusion matrices on the Test.
Produce a “well documented and explained” R Markdown knit file analyzing the data with findings on the
model with the highest classification ability. Also describe the features of the categories that are not classified
correctly. Create a confusion matrix to answer the last question and run descriptive statistics on the
misclassified categories. Provide any necessary EDA and visuals to enhance understanding of your analysis.
Instructor: Prashant Mittal.

Purchase answer to see full

Explanation & Answer:
1 program



Lending Club

User generated content is uploaded by users for the purposes of learning and should be used following Studypool’s honor code & terms of service.

Looking for this assignment?

do my essay homework

Reviews, comments, and love from our customers and community

Article Writing

Great service so far. Keep doing what you do, I am really impressed by the work done.



PowerPoint Presentation

I am speechless…WoW! Thank you so much! Definitely, the writer is talented person. She provided me with an essay a day early before the due date!

Stacy V.

Part-time student

Dissertation & Thesis

This was a very well-written paper. Great work fast. I was in pretty desperate need for help to finish this paper before the due date, which was in nine hours.

M.H.H. Tony


Annotated Bibliography

I love working with this company. You always go above and beyond and exceed my expectations every time. Kate did a WONDERFUL job. I would highly recommend her.

Francisca N.


Book Report / Review

I received my order wayyyyyyy sooner than I expected. Couldn’t ask for more. Very good at communicating & fast at replying. And change & corrections she put in the effort to go back and change it!

Mary J.


Essay (Any Type)

On time, perfect paper. All concerns & matters I had Tom was able to answer them! I will definitely provide him with more orders!

Prof. Kate (Ph.D)


Case Study

Awesome! Great papers, and early! Thank you so much once again! Definitely recommend to trust James with your assignments! He won’t disappoint!

Kaylin Green


Proofreading & Editing

Thank you Dr. Rebecca for editing my essays! She completed my task literally in 3 hours. For sure will work with her again, she is great and follows all instructions

Rebecca L.


Critical Thinking / Review

Extremely thorough summary, understanding and examples found for social science readings, with edits made as needed and on time. It’s like having a tutoring service available (:

Arnold W.



Perfect!I only paid about $80, which i think was a good price considering what my paper entailed. My paper was done early and it was well written!

Joshua W.


Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>