The use of machine learning models for loan approval predictions is a prime application of classification modelling in the loan application process. The classification model can learn patterns from ground truth to predict new loan application outcomes. The models can provide quicker and more data-driven decisions by automating the loan application process by predicting if a credit/loan applicant should be approved or declined based on a set of features.
In this project, a loan approval classification model is developed to evaluate a credit/loan applicant’s ability and willingness to repay a loan. The model approves or declines an applicant's application once the applicant's information is entered , speeding up the loan underwriting process.
The model is continuously integrated and deployed automatically by leveraging server-dependent resources (Jenkins and Ansible) and cloud resources(AWS EC2 and AWS S3 Bucket).
- Python 3.11
- Jenkins
- Docker
- Cloud platform account (AWS)
- .env file with AWS account credentials (Secret key and Access Key)
- Clone the repository:
git clone https://github.com/jibbs1703/Loan-Approval-Prediction.git
cd Loan-Approval-Prediction
- Install Python dependencies:
pip install -r requirements.txt