agrisampath/credit-survival-risk

Here, we are building a credit risk analytic model using statistical method - **Survival analysis**. Survival analysis is a popular analytic methodology to investigate the expected duration of time until an event of interest occurs.

Credit-Survival-Risk

Credit-Survival-Risk is a project aimed at building a credit risk analytic model using the statistical method of Survival Analysis. Survival analysis is a popular analytical methodology used to investigate the expected duration of time until an event of interest occurs.

Overview

The Credit-Survival-Risk project focuses on analyzing credit risk by leveraging survival analysis techniques. Traditional credit risk assessment methods often rely on static measures that do not consider the timing of default or delinquency. Survival analysis, on the other hand, takes into account the time dimension and enables a more dynamic approach to credit risk evaluation.

This project aims to develop a robust credit risk model that can predict the likelihood and timing of credit default or delinquency. By utilizing survival analysis techniques, we can incorporate various factors such as borrower characteristics, economic indicators, and other relevant variables to create a comprehensive and accurate risk assessment model.

Features

• Survival Analysis: The project employs survival analysis as the primary statistical method to model credit risk.
• Dynamic Time-to-Event Modeling: Dynamic Time-to-Event Modeling: Unlike traditional static credit risk models, the survival analysis approach considers the timing of default or delinquency events.
• Incorporation of Relevant Factors: Incorporation of Relevant Factors: The model incorporates various borrower's payment behavior, and other relevant variables to enhance risk assessment accuracy.
• Predictive Modeling: Predictive Modeling: The credit risk model aims to provide accurate predictions of the likelihood and timing of credit default or delinquency.
• Data Exploration and Preprocessing: Data Exploration and Preprocessing: The project includes comprehensive data exploration and preprocessing steps to ensure data quality and prepare it for analysis.
• Model Tracking using MLFlow: Model Tracking using MLFlow allows for the systematic recording and management of the development, training, and deployment of credit risk models, enabling reproducibility, experimentation, and collaboration among data scientists.

MLFlow

Installation

To run the Credit-Survival-Risk project, follow these steps:

1. Clone the repository:

git clone https://github.com/yewleongtoh/Credit-Survival-Risk.git

2. Install the required dependencies:

pip install -r requirements.txt

3. View it in Jupyter Notebook

Contributing

Contributions to the Credit-Survival-Risk project are welcome and encouraged! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your contribution:

git checkout -b feature/your-feature-name

3. Make your changes and commit them:

git commit -m "Add your message here"

4. Push your changes to your fork:

git push origin feature/your-feature-name

5. Open a pull request to the main repository's master branch, describing your changes and their purpose.