Loan Default Prediction

Table of Contents

• Technologies Used
• Description
• Objectives
• Presentation
• Notebooks Overview
• Using Docker
• Installation
• Usage
• Project Structure
• Collaborators
• License

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Technologies Used

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Description

This project is focused on building an app that estimates the probability of default for each client based on their characteristics. Then create a CI/CD pipeline to deploy it with AWS Elastic Container Service

Objectives

The main objective of this project is build an image and a pipeline with all the necessary tools to achieve the deployment of a predictive app. The specific steps include:

1. Data Preprocessing: Filtering and preparing the dataset for analysis.
2. Model Engineering: Test at least two classification algorithms.
3. Model Tracking: Tracking the metrics and artifacts of the model with MLflow.
4. App Deployment: Create a CI/CD pipeline.

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Presentation

A presentation is available as a PDF file in the repo Loan_Default_Prediction_MLOPS_Presentation.pdf & also as a Canva/Powerpoint presentation through the following link: Presentation Link.

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Notebooks Overview

1. ¨Exploratory_Data_Analysis.ipynb:
   • This notebook contains the data exploration, analysis & preprocessing.
2. ¨Loan_Default_Prediciont_Models.ipynb:
   • This notebook contains the models fitting, evaluation and mlflow setup.

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Using Docker

1. Pulling the Docker Image

To pull the Docker image from Docker Hub, run the following command:

# Pull the docker image
$ docker pull medkallel/loan-default-prediction:latest

2. Building the Docker Image

If you prefer to build the Docker image locally, navigate to the project directory and run:

# Build the docker image
$ docker build -t loan-default-prediction .

3. Running the Docker Container

To run the Docker container, use the following command:

# Run the docker container
$ docker run -p 5000:5000 loan-default-prediction

Tip

You can access the app on another device by following the link: http://<server-ip>:8501

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Installation

Important

The project was done on Python 3.11.6

To run this project locally, follow these steps:

1. Clone the repository:

# Clone the repository
$ git clone https://github.com/Medkallel/Loan-Default-Prediction.git
# Navigate into the directory
$ cd Loan-Default-Prediction

2. Install the required dependencies:

# Install the requirements
$ pip install -r requirements.txt

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Usage

1. To use the App, Just run the app.py

$ python app.py

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Project structure

📦 mlops-project/
├── 📁.github/
│   └── 📁workflows/
│       └── aws.yaml
├── 📁Data/
│   ├── 🗃️Loan_Data_Describe.csv # Describe data used to denormalize
│   ├── 🗃️Loan_Data.csv # Initial Dataset
│   └── 🗃️Processed_Loan_Data.csv 
│ 
├── 📁Models/
│   └── 🤖model.pkl
│
├── 📁mlartifacts/ # Contains mlflow artifacts for runs
├── 📁mlruns/ # Contains mlflow runs logs
│ 
├── 📁templates/
│   └── index.html
├── 🐍app.py
├── 📓Exploratory_Data_Analysis.ipynb
├── 📓Loan_Default_Prediction_Models.ipynb
├── 📄requirements.txt
├── 📄.dockerignore
├── 📄.gitignore
├── 📄Dockerfile
├── 📄LICENCE.md
├── 📄README.md
└── 📄Loan_Default_Prediction_MLOPS_Presentation.pdf

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Collaborators

This project was developed by a collaborative team. Each member played a crucial role in the research, development, and analysis:

• Mohamed Kallel
• Jean Christophe Rigoni
• Simon Pierre Rodner

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License

This project is under the CC BY-NC 4.0 License. For more information, refer to the license file.