Credit risk refers to the chance that a borrower will be unable to make their payments on time and default on their debt. It refers to the risk that a lender may not receive their interest due or the principal lent on time.
Read in my blogs https://medium.com/mlearning-ai/credit-risk-modelling-in-python-7b21a0b794b1
The objective of this project is to predict the probability of default on a given obligation, in this case, credit cards. This will allow the generation of strategies that minimize the risk of deterioration of the client's financial health.
- Data preparation and Pre-processing
- Checking missing values
- Outliers removal
- Encoding concept
- Feature Engineering and Selection
- features selection
- Scaling the features
- Balancing the data
- Model Development and Model Evaluation
- Logistic regression
- Random Forest
- Xgboost
- Hyperparameter tuning
- Cross validation
- numpy
- matplotlib
- scikit-learn
- XGBOOST
- seaborn
- pyhthon 3.9
Just run jupyter notebook in terminal and it will run in your browser.
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Install Jupyter here i've you haven't.
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install xgboost by using
pip install xgbin command line prompt/ anconda i've you haven't.
- Logistic regression
- Random Forest
- Xgboost Classifer
- Cross validation
- we have acheived the final model accuracy 82%
- Clone or download the repo.
- Open command prompt in the downloaded folder
We have built the credit risk model by using python. we have tried with different machine learning algorithms ie. Logistic regression, Random Forest, and Xgboost classifier. we have also done hyperparameter tuning and cross-validation. The final accuracy of the model we achieved was 82%.