https://github.com/balavenkatesh3322/loan-default-prediction
An end-to-end machine learning project to predict loan default risk. Includes Exploratory Data Analysis (EDA), feature engineering, a Gradient Boosting model, and a proposed system architecture for deployment.
https://github.com/balavenkatesh3322/loan-default-prediction
data-science deep-learning feature-engineering gradient-boosting loan-default-prediction machine-learning scikit-learn tutorial-exercises
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An end-to-end machine learning project to predict loan default risk. Includes Exploratory Data Analysis (EDA), feature engineering, a Gradient Boosting model, and a proposed system architecture for deployment.
- Host: GitHub
- URL: https://github.com/balavenkatesh3322/loan-default-prediction
- Owner: balavenkatesh3322
- Created: 2025-07-07T09:31:09.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2025-07-07T09:51:17.000Z (12 months ago)
- Last Synced: 2025-07-07T10:56:17.351Z (12 months ago)
- Topics: data-science, deep-learning, feature-engineering, gradient-boosting, loan-default-prediction, machine-learning, scikit-learn, tutorial-exercises
- Language: Jupyter Notebook
- Homepage:
- Size: 12.4 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Loan Default Prediction
An end-to-end machine learning project to predict loan default risk. Includes Exploratory Data Analysis (EDA), feature engineering, a Gradient Boosting model, and a proposed system architecture for deployment.
---
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [System Architecture](#system-architecture)
- [Model Performance](#model-performance)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
---
## Project Overview
For financial institutions, the ability to predict loan defaults is a critical factor in managing financial risk and maintaining profitability. This project addresses this challenge by building a robust machine learning model to predict the likelihood of a borrower defaulting on a loan.
The solution involves a complete MLOps workflow, from data ingestion and analysis to model training, deployment, and monitoring. By leveraging a Gradient Boosting Classifier, the model provides a reliable risk score that can be integrated into a real-time loan application system.
## Features
* **Exploratory Data Analysis (EDA):** A detailed analysis of the dataset is provided in the `eda.ipynb` notebook, including visualizations of feature distributions and correlations.
* **Data Cleaning & Preprocessing:** A robust pipeline handles missing values, converts data types, and scales features to prepare the data for modeling.
* **Feature Engineering:** New, insightful features were created to improve model accuracy, such as `Credit_to_Income_Ratio` and `Employment_Years`.
* **Imbalanced Data Handling:** The project uses the **SMOTE (Synthetic Minority Over-sampling Technique)** to address the significant class imbalance in the dataset, ensuring the model learns to identify the minority class (defaulters) effectively.
* **Model Training & Tuning:** A Gradient Boosting Classifier is trained and optimized using `GridSearchCV` to achieve the best possible performance.
* **End-to-End MLOps Pipeline:** The project includes a proposed system architecture for a complete CI/CD pipeline, covering everything from automated training to model deployment and monitoring.
---
## System Architecture
The proposed architecture for deploying the model into a production environment is designed for scalability, reliability, and continuous improvement. It consists of an automated ML pipeline for training and a serving infrastructure to deliver real-time predictions.
```mermaid
graph TD
A[Data Sources] --> B{Data Ingestion};
B --> C[Data Preprocessing & Feature Engineering];
C --> D[Model Training & Tuning];
D --> E[Model Registry];
E --> F[Model Deployment];
F --> G[API Endpoint];
G --> H[Loan Application];
subgraph "ML Pipeline"
B;
C;
D;
end
subgraph "Serving Infrastructure"
E;
F;
G;
end
subgraph "Monitoring"
M[Model Monitoring] --> N[Alerting];
F --> M;
end
```
---
## Model Performance
The final Gradient Boosting model was evaluated on a hold-out test set. The key performance metrics are:
* **AUC-ROC Score:** **[Run the `eda.ipynb` notebook to generate this value]**
Below are the visualizations of the model's performance and the most important features driving its predictions.
*(To generate these images, please run the `eda.ipynb` notebook.)*
**ROC Curve**
**Top Most Important Features**
---
## Getting Started
Follow these instructions to set up and run the project on your local machine.
### Prerequisites
* Python 3.7+
* Jupyter Notebook or JupyterLab
### Installation
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/Loan-Default-Prediction.git
cd Loan-Default-Prediction
```
2. **Install the required packages:**
```bash
pip install -r requirements.txt
```
*(Note: A `requirements.txt` file is recommended. For now, you can install the packages listed in the notebook.)*
---
## Usage
To explore the data, train the model, and see the results, run the Jupyter notebook:
```bash
jupyter notebook eda.ipynb
```
Inside the notebook, you can execute the cells sequentially to see the entire process, from data loading to model evaluation.