Banking Risk Assessment ML

Overview

This repository hosts a machine learning project aimed at assessing credit risk in the banking industry. The project utilizes data science and machine learning techniques to predict the creditworthiness of banking clients, aiding in effective risk management.

Contents

• notebooks/: Contains Jupyter notebooks used for exploratory data analysis, model building, and evaluation.

  • credit-risk-assesment-ml-based.ipynb: Main notebook with detailed steps of data preprocessing, model building, training, and evaluation.

• src/: Includes Python scripts for the implementation of machine learning models.

  • credit_risk_assesment_ml.py: Python script with functions and model definitions.

Setup and Installation

To set up this project, clone the repository and install the necessary dependencies.

Clone the repository:

git clone https://github.com/[username]/BankingRiskAssessmentML.git

Navigate to the project directory:

cd BankingRiskAssessmentML

Install dependencies (preferably in a virtual environment):

pip install -r requirements.txt

Usage

• Run the Jupyter notebook for a comprehensive walkthrough of the data analysis and modeling process:

jupyter notebook notebooks/credit-risk-assesment-ml-based.ipynb

• For running the Python script:

python src/credit_risk_assesment_ml.py

Contributing

Contributions to this project are welcome. Please open an issue or a pull request to suggest improvements or add new features.

License

This project is licensed under the MIT License.