In this notebook are applied several Machine Learning algorithms in order to predict whether a loan case will be paid off or not.
A historical dataset(provided by IBM) from previous loan applications is fed to different classifiers, after a previous data cleaning and manipulation. The goal is to evaluate the accuracy of each algorithm and check which one presents a better performance in the stated objective.
The following algorithms will be used:
- K-Nearest Neighbour
- Decision Tree
- Support Vector Machine
- Logistic Regression
To measure the accuracy of each classifier, the following metrics will be used:
- Jaccard index
- F1-score
- LogLoss
This dataset is about past loans. The data set includes details of 346 customers whose loan are already 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 at the |
| 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 |
Training set: loan_train.csv
Test set: loan_test.csv