Homework # 3 (200 points)
For this assignment, we will be working with two different datasets. For problem # 1, we will
still be working with the Penguin dataset. For problem # 2-4, we will be working with the
attached dataset on loan approval status.
1. Please use K-nearest neighbor (KNN) algorithm to predict the species variable. Please
be sure to walk through the steps you took. (40 points)
2. Please use the attached dataset on loan approval status to predict loan approval using
Decision Trees. Please be sure to conduct a thorough exploratory analysis to start the
task and walk us through your reasoning behind all the steps you are taking. (40 points)
3. Using the same dataset on Loan Approval Status, please use Random Forests to predict
on loan approval status. Again, please be sure to walk us through the steps you took to
get to your final model. (50 points)
4. Using the Loan Approval Status data, please use Gradient Boosting to predict on the
loan approval status. Please use whatever boosting approach you deem appropriate;
but please be sure to walk us through your steps. (50 points)
5. Model performance: please compare the models you settled on for problem # 2 – 4.
Comment on their relative performance. Which one would you prefer the most? Why?
(20 points)
The loan approval status data dictionary:
Variable Description
Loan_ID Unique Loan ID
Gender Male/ Female
Married Applicant married (Y/N)
Dependents Number of dependents
Education Applicant Education (Graduate/ Under Graduate)
Self_Employed Self employed (Y/N)
ApplicantIncome Applicant income
CoapplicantIncome Coapplicant income
LoanAmount Loan amount in thousands
Loan_Amount_Term Term of loan in months
Credit_History credit history meets guidelines
Property_Area Urban/ Semi Urban/ Rural
Loan_Status (Target) Loan approved (Y/N)
