https://github.com/aysenurcftc/credit_score-classification
https://github.com/aysenurcftc/credit_score-classification
classification credit-scoring csv finance logistic-regression neural-network nn scikit-learn
Last synced: 14 days ago
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- Host: GitHub
- URL: https://github.com/aysenurcftc/credit_score-classification
- Owner: aysenurcftc
- Created: 2024-05-22T17:03:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-06T09:29:40.000Z (over 1 year ago)
- Last Synced: 2025-01-14T10:27:18.924Z (9 months ago)
- Topics: classification, credit-scoring, csv, finance, logistic-regression, neural-network, nn, scikit-learn
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Credit Score Classification
The credit score classification was performed using five different datasets.
## Installation
```bash
pip install -r requirements.txt
```
## Usage/Examples
```
usage: main.py [-h] [--run_mode RUN_MODE] [--dataset DATASET] [--model_type MODEL_TYPE] [--imputing_type_numerical IMPUTING_TYPE_NUMERICAL]
[--imputing_type_categorical IMPUTING_TYPE_CATEGORICAL] [--split_ratio SPLIT_RATIO] [--filepath FILEPATH]
Train a model on a specified dataset.
options:
-h, --help show this help message and exit
--run_mode RUN_MODE Mode to run the script in, e.g., training or testing
--dataset DATASET Dataset to use for training
--model_type MODEL_TYPE
Type of model to use, e.g., 'logistic regression' or 'NN'
--imputing_type_numerical IMPUTING_TYPE_NUMERICAL
Imputing strategy for numerical features
--imputing_type_categorical IMPUTING_TYPE_CATEGORICAL
Imputing strategy for categorical features
--split_ratio SPLIT_RATIO
Train-test split ratio
--filepath FILEPATH Path to the dataset file
None
usage: main.py [-h] [--run_mode RUN_MODE] [--dataset DATASET] [--model_type MODEL_TYPE] [--imputing_type_numerical IMPUTING_TYPE_NUMERICAL]
[--imputing_type_categorical IMPUTING_TYPE_CATEGORICAL] [--split_ratio SPLIT_RATIO] [--filepath FILEPATH]
Train a model on a specified dataset.
options:
-h, --help show this help message and exit
--run_mode RUN_MODE Mode to run the script in, e.g., training or testing
--dataset DATASET Dataset to use for training
--model_type MODEL_TYPE
Type of model to use, e.g., 'logistic regression' or 'NN'
--imputing_type_numerical IMPUTING_TYPE_NUMERICAL
Imputing strategy for numerical features
--imputing_type_categorical IMPUTING_TYPE_CATEGORICAL
Imputing strategy for categorical features
--split_ratio SPLIT_RATIO
Train-test split ratio
--filepath FILEPATH Path to the dataset file
```
## Deployment
To deploy this project run
```bash
docker build -t ml_proje .
docker run ml_proje --run_mode training --dataset statlog_german_credit_data --model_type NN --imputing_type_numerical median --imputing_type_categorical mode --split_ratio 0.7 --filepath data/statlog_german_credit_data.csv
```