Skip to content

Latest commit

 

History

History
32 lines (23 loc) · 1.44 KB

README.md

File metadata and controls

32 lines (23 loc) · 1.44 KB
Raw

Loan-Status-Prediction-using-Classification-Algorithms

$\textcolor{#246EAA}{\textsf{This supervised classification project was carried out as part of the IBM Data Science Professional certificate.}}$

https://www.coursera.org/professional-certificates/ibm-data-science

$\textcolor{#246EAA}{\textsf{Loan Status Prediction using Classification Algorithms}}$:

  • KNN,
  • Decision Tree
  • SVM,
  • Logistic Regression.

$\textcolor{#246EAA}{\textsf{Determination of the best classifier. No optimization of hyperparameters.}}$
$\textcolor{#246EAA}{\textsf{The dataset is about past loans, it includes details of 346 customers whose loans are already}}$ $\textcolor{#246EAA}{\textsf{paid off or defaulted. It includes following fields}}$:

Field Description
Loan_status Whether a loan is paid off on in collection
Principal Basic principal loan amount
Terms Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule
Effective_date When the loan got originated and took effects
Due_date Since it’s one-time payoff schedule, each loan has one single due date
Age Age of applicant
Education Education of applicant
Gender The gender of applicant

The Jupyter Notebook is available here

Stephane D. Oct-2022