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Maching Learning Project : A classification problem using K Nearest Neighbor(KNN), Decision Tree, Support Vector Machine, and Logistic Regression algorithms
In this Mini Project, we'll try to practice all the classification algorithms that we learned in the Machine Learning With Python course. So, we'll load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.
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 |
You can download the dataset Loan_train.csv by clicking here
We used:
For building our models we will be using the folowing algorithms:
The report below shows the accuracies of all built models using different evaluation metrics:
| Algorithm | Jaccard | F1-score | LogLoss |
|---|---|---|---|
| KNN | 0.67 | 0.63 | NA |
| Decision Tree | 0.76 | 0.77 | NA |
| SVM | 0.80 | 0.76 | NA |
| Logistic Regression | 0.74 | 0.70 | 0.67 |
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