Loan Approval Prediction Deployment with AWS

This project provides an end-to-end solution for predicting loan approvals using machine learning and deploying the model on Amazon Web Services (AWS). It includes data preprocessing, model training, and deployment steps, ensuring a robust and scalable system

Overview

Loan approval is a critical process for financial institutions, requiring precise and efficient decision-making. This project uses a machine learning model to predict whether a loan application is likely to be approved. The model is deployed using AWS, making it accessible as a scalable prediction service.

Features

Data Preprocessing: Handles missing values, encodes categorical variables, and scales numerical data.

Machine Learning Model: Trained on historical loan data to predict loan approval outcomes.

API Service: Exposes an API endpoint for making predictions.

Dockerized Application: Ensures consistency and portability across development and production environments.

AWS Deployment: Deployed on AWS services for scalability and reliability.

CI/CD Integration: Automates testing and deployment using GitHub Actions.

Technologies Used

• Libraries
  • Pandas , Numpy ( Data Preprocessing)
  • Scikit-learn (Model training and evaluation)
  • FastAPI (API development)

Cloud Platform: AWS (ECR, EC2, etc.)

Containerization: Docker

Automation: GitHub Actions for CI/CD

Setup and Installation

Clone the Repository:

git clone : https://github.com/Adelakun1999/loan-prediction-deployment-with-AWS.git

cd loan-prediction-deployment-with-AWS

Install Dependencies: Ensure you have Python 3.8+ installed. Then run:

pip install -r requirements.txt

Run Locally: Start the application locally:

phyton main.py

Test API Locally: Use tools like Postman or cURL to test the API:

curl -X POST -H "Content-Type: application/json" -d '{"data": [<sample_input>]}' http://localhost:8005/predict

Example of input data

{
  "Dependents": 1,
  
  "Education": "Graduate",
  
  "Self_Employed": "Yes",
  
  "TotalIncome": 2000000,
  
  "LoanAmount": 350000,
  
  "Loan_Amount_Term": 77800,
  
  "Credit_History": 2560000000,
  
  "Residential_Assets_Value": 30000000,
  
  "Commercial_Assets_Value": 5000000,
  
  "Luxury_Assets_Value": 23000000,
  
  "Bank_Asset_Value": 50000000
}