Description
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.
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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
irresponsibly.
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.
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Lending Club
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