https://github.com/anas436/loan-case-will-be-paid-off-or-not-prediction-using-classification-with-python
decision-trees f1-score ibm-watson itertools jaccard-score knearest-neighbor-algorithm log-loss logistic-regression matplotlib-pyplot numpy pandas python3 scikit-learn sklearn support-vector-machines
Last synced: 6 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/anas436/loan-case-will-be-paid-off-or-not-prediction-using-classification-with-python
- Owner: Anas436
- Created: 2022-09-13T19:32:55.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-09-13T20:10:09.000Z (about 3 years ago)
- Last Synced: 2025-02-01T15:30:40.551Z (8 months ago)
- Topics: decision-trees, f1-score, ibm-watson, itertools, jaccard-score, knearest-neighbor-algorithm, log-loss, logistic-regression, matplotlib-pyplot, numpy, pandas, python3, scikit-learn, sklearn, support-vector-machines
- Language: Jupyter Notebook
- Homepage:
- Size: 56.6 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Loan-Case-will-be-Paid-OFF-or-NOT-Prediction-using-Classification-with-Python
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:
* k-Nearest Neighbour
* Decision Tree
* Support Vector Machine
* Logistic Regression
The results is reported as the accuracy of each classifier, using the following metrics when these are applicable:
* Jaccard index
* F1-score
* 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 |