The likelihood that a borrower would not repay their loan to the lender is known as Credit Risk. It is measured in probability, i.e. a value from 0 - 1 but to make it easy to understand for everyone, the probability is converted into a score known as Credit Score.
Higher the probability of not repaying the loan (default event), lower will be the Score and vice-versa.
Minimum score is 300 and maximum score is 850 as adopted by FICO.
In this project I have created a machine learning model which predicts the probability of repayment of loan by the borrower. Further it is converted into a score between 300 and 850.
Data - https://www.kaggle.com/imsparsh/lending-club-loan-dataset-2007-2011
Language - Python
Libraries Used - NumPy, Pandas, Matplotlib, Seaborn, Sci-kit learn
- Credit_score_modeling_stagePreprocessing.ipynb
- Credit_score_modeling_stageModeling.ipynb