https://github.com/sakaars/risk-assessment-system

https://github.com/sakaars/risk-assessment-system

Last synced: 9 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/sakaars/risk-assessment-system
• Owner: sakaars
• Created: 2025-01-10T21:44:23.000Z (27 days ago)
• Default Branch: main
• Last Pushed: 2025-01-10T21:50:20.000Z (27 days ago)
• Last Synced: 2025-01-10T22:40:42.920Z (27 days ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Financial Risk Assessment

## Description

This project aims to assess financial risk using Python and various data science techniques. It includes data loading, exploration, Value at Risk (VaR) calculation, stress testing, visualization, and database integration. The project utilizes the "Financial Risk Assessment" dataset from Kaggle to demonstrate these techniques.

## Dataset

The dataset used in this project is the "Financial Risk Assessment" dataset available on Kaggle. You can download it using the following command after setting up your Kaggle API credentials:

## Requirements

- Python 3

- Libraries: pandas, numpy, matplotlib, scipy, sqlalchemy, pymysql, psycopg2-binary, kaggle

To install the necessary libraries, you can use the following command in your Colab notebook:

## Usage

1. **Set up Kaggle API:** 

   - Download your `kaggle.json` file from your Kaggle account.

   - Upload it to your Colab notebook using `files.upload()`.

   - Move the file to the correct directory and set permissions:

2. **Download and unzip the dataset:** Use the command mentioned in the "Dataset" section to download the dataset. Then, unzip it using:

3. **Run the code:** Execute the code cells in the Colab notebook to perform the analysis. This includes data loading, exploration, VaR calculation, stress testing, and visualization.

4. **Interpret results:** Analyze the calculated VaR, stress test results, and visualizations to understand the financial risk.

## Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.