https://github.com/arya2004/credit-score-classification
Credit Score Classification in R using various algorithms
https://github.com/arya2004/credit-score-classification
knn-classification random-forest svm-classifier
Last synced: 7 months ago
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Credit Score Classification in R using various algorithms
- Host: GitHub
- URL: https://github.com/arya2004/credit-score-classification
- Owner: arya2004
- License: cc0-1.0
- Created: 2024-03-12T18:46:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-26T14:49:17.000Z (9 months ago)
- Last Synced: 2025-02-12T16:18:16.644Z (9 months ago)
- Topics: knn-classification, random-forest, svm-classifier
- Language: R
- Homepage:
- Size: 37.1 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Credit Score Classification
This repository contains the code, data, and models for a credit score classification project. The goal of this project is to build and evaluate machine learning models to predict credit scores based on provided data.
## Repository Structure
- `data/`
- `processed/`:
- This directory will contain processed datasets.
- `raw/`:
- `old_test.csv`: Previous version of the test dataset.
- `old_train.csv`: Previous version of the train dataset.
- `test.csv`: Current test dataset.
- `train.csv`: Current train dataset.
- `models/`:
- `f_model.rds`: Final model saved as an RDS file.
- `mlr.rds`: Multi-layer perceptron model.
- `old_rf_model.rds`: Old random forest model.
- `smote_model.rds`: Model trained with SMOTE.
- `v2_smote_model.rds`: Version 2 of the SMOTE model.
- `reports/`:
- `newplot.png`: Plot generated during the analysis.
- `src/R/`:
- `data_preprocessing.ipynb`: Jupyter notebook for data preprocessing.
- `logistic_regression.ipynb`: Jupyter notebook for logistic regression model.
- `naive_bayes.ipynb`: Jupyter notebook for Naive Bayes model.
- `old_data_preprocessing.ipynb`: Old version of data preprocessing.
- `old_logistic_regression.ipynb`: Old version of logistic regression model.
- `old_naive_bayes.ipynb`: Old version of Naive Bayes model.
- `old_random_forest.ipynb`: Old version of random forest model.
- `old_svm.ipynb`: Old version of support vector machine model.
- `random_forest.ipynb`: Jupyter notebook for random forest model.
- `website/`:
- `webapp.ipynb`: Jupyter notebook for creating a web application.
- `.gitignore`: Git ignore file to exclude unnecessary files from version control.
- `CONTRIBUTING.md`: Guidelines for contributing to this repository.
- `README.md`: This file.
## Getting Started
### Prerequisites
- R and RStudio
- Jupyter Notebook
- Required R libraries (listed in the notebooks)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/arya2004/credit_score_classification.git
```
2. Navigate to the project directory:
```bash
cd credit_score_classification
```
3. Install the required libraries. You can find the list of required libraries in each Jupyter notebook (e.g., `random_forest.ipynb`).
### Usage
1. **Data Preprocessing**:
- Use `src/R/data_preprocessing.ipynb` to preprocess the raw data.
2. **Model Training**:
- Train different models using the respective notebooks in `src/R/` (e.g., `logistic_regression.ipynb`, `naive_bayes.ipynb`, `random_forest.ipynb`).
3. **Model Evaluation**:
- Evaluate the models and compare their performance.
4. **Web Application**:
- Create a web application using `website/webapp.ipynb` to deploy the final model.
## Contributing
Please read the `CONTRIBUTING.md` file for guidelines on contributing to this project.
## License
This project is licensed under the CC0-1.0 license - see the `LICENSE.md` file for details.
## Acknowledgements
- Thanks to Professor Abha Marathe who guided this project.
- Thanks to all the contributors.