In this project, you will complete a notebook where you will build a classifier to predict whether a loan case will be paid off or not.
You load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models:
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k-Nearest Neighbour
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Decision Tree
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Support Vector Machine
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Logistic Regression
The results is reported as the accuracy of each classifier, using the following metrics when these are applicable:
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Jaccard index
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F1-score
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LogLoass
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 |