https://github.com/prakhar-ff13/credit-risk-modelling
Machine Learning Case Study on Credit Loans.
https://github.com/prakhar-ff13/credit-risk-modelling
data-analysis data-science data-visualization jupyter-notebook machine-learning machine-learning-algorithms python3
Last synced: 6 months ago
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Machine Learning Case Study on Credit Loans.
- Host: GitHub
- URL: https://github.com/prakhar-ff13/credit-risk-modelling
- Owner: Prakhar-FF13
- Created: 2018-07-16T11:17:41.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-16T17:10:46.000Z (over 7 years ago)
- Last Synced: 2025-05-07T10:15:08.349Z (6 months ago)
- Topics: data-analysis, data-science, data-visualization, jupyter-notebook, machine-learning, machine-learning-algorithms, python3
- Language: Jupyter Notebook
- Size: 7.23 MB
- Stars: 6
- Watchers: 0
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
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README
# Credit Risk Modelling
We are building ML model which can help us in order get an idea, whether a person will be doing any default activity for his loan in next 2 year.
## Dependency Libraries
* Python 3.x
* pandas
* numpy
* scipy
* scikit-learn
* matplotlib
* seaborn
* jupyter notebook
## Installation Commands
#### If using pip ->
```bash
Pandas: - pip install pandas
numpy: - pip install numpy
scipy: - pip install scipy
scikit-learn: - pip install scikit-learn
matplotlib: - pip install matplotlib
seaborn: - pip install seaborn
jupyter notebook: - pip install jupyter
```
#### If using anaconda ->
```bash
Pandas: - conda install pandas
numpy: - conda install numpy
scipy: - conda install scipy
scikit-learn: - conda install scikit-learn
matplotlib: - conda install matplotlib
seaborn: - conda install seaborn
jupyter notebook: - conda install jupyter
```
### Dataset obtained from - https://github.com/jalajthanaki/credit-risk-modelling/tree/master/data