Using sklearn module to create and evaluate various machine learning models. This is the final project of the Coursera's Machine Learning with Python course.
This dataset is about past loans. The Loan_train.csv 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 |
We use the training set to build an accurate model and then use the test set to report the accuracy of the model using the following algorithms:
- K Nearest Neighbor(KNN)
- Decision Tree
- Support Vector Machine
- Logistic Regression
| Algorithm | Jaccard | F1-score | LogLoss |
|---|---|---|---|
| KNN | 0.703704 | 0.686067 | NA |
| Decision Tree | 0.796296 | 0.780146 | NA |
| SVM | 0.722222 | 0.621266 | NA |
| Logistic Regression | 0.740741 | 0.630418 | 0.493198 |