Build Credit Risk predictor using Federated Learning
by kandikits Updated: Apr 3, 2023
Federated Learning can train machine learning models on data from different hospitals, banks and autonomous vehicles without sharing sensitive data. But how do you create a Federated learning application? The answer is the kandi 1-click solution kit for Credit-risk-federated-learning.
Certainly, Federated Learning can be applied in the credit risk scenario to improve credit risk models' accuracy without compromising customer data privacy.
Banks collect and centralize customer data to train their credit risk models in the traditional approach. However, this approach can be challenging due to regulatory compliance, data privacy, and security concerns. Federated Learning addresses these challenges by allowing banks to train their credit risk models on customer data without transferring it to a centralized location.
This fully editable source code builds your Credit risk federated learning in minutes. The entire solution is available as a package to download from the source code repository.
Federated Learning in credit risk scenarios can have several benefits, including:
- Improved accuracy: Federated Learning allows banks to train models on a larger and more diverse dataset, leading to better accuracy.
- Data privacy: Federated Learning ensures that sensitive customer data is kept private and secure, which is critical in the context of credit risk.
- Regulatory compliance: Federated Learning can help banks comply with regulations around data privacy and security.
The instructions for running a Credit Risk Federated Learning using this kit are added in this section. The entire solution is available as a package to download from the source code repository.
For Windows OS,
- Download, extract the zip file and run. Do ensure to extract the zip file before running it. The installation may take from 10 to 20 minutes based on network bandwidth.
- After successful installation of the kit, press 'Y' to run the kit and follow the step provided in the notebook.
- To run the kit manually, press 'N' and locate the folder 'credit-risk-federated-learning' in the "C:/kandikits/credit-risk-federated-learning" location
- Navigate to the directory 'credit-risk-federated-learning'.
- Open command prompt in the extracted directory 'credit-risk-federated-learning' and run the server.bat and client.bat file to execute the training process of federated learning.
- Locate and open the 'Inference.ipynb' notebook in the current directory and Run All cells to validate the model on unseen data.
- Please follow the steps provided in the "credit-risk-federated-learning.ipynb" notebook to execute.
For other Operating System,
- Click here to download python
- Click here to download the repository
- Extract the zip file and navigate to the directory 'credit-risk-federated-learning'
- Run the following commands to install Python
tar -xf python*.tar.gz cd python3.* ./configure sudo make install
- Open the terminal in the extracted directory 'credit-risk-federated-learning'
- Create and activate a virtual environment by these commands:
python3.9 -m venv example source example/bin/activate
- Install dependencies by executing the command
pip install -r requirements.txt
- Run the command ‘jupyter notebook’ and select the notebook 'credit-risk-federated-learning.ipynb' to open and follow the steps to implement Federated Learning.
Click on the button below to download the solution and follow the deployment instructions to begin setup. This 1-click kit has all the required dependencies and resources to build your Credit risk federated learning App.
- Install the Microsoft Visual C++ Redistributable for Visual Studio 2022 in case the kit doesn't successfully run on your windows system.
- In case, step 1 doesn't solve your issue, set up Microsoft build Tools.
For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community
VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.
Jupyter Interactive Notebook
Jupyter Notebook 10143 Version:v7.0.0b3 License: Permissive (BSD-3-Clause)
Numpy and Pandas are powerful tools for data preprocessing in machine learning. They provide tools for handling missing data, feature scaling, one-hot encoding, data normalization, and transformation.
These tools can help you to prepare your data for machine learning and improve the performance of your models.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python 38577 Version:v2.0.2 License: Permissive (BSD-3-Clause)
The fundamental package for scientific computing with Python.
Python 23692 Version:v1.25.0rc1 License: Permissive (BSD-3-Clause)
Scikit-learn is a powerful and versatile machine learning library in Python that provides a wide range of tools and algorithms for building and training machine learning models. It is widely used in academia and industry for various machine learning applications.
scikit-learn: machine learning in Python
Python 54472 Version:1.2.2 License: Permissive (BSD-3-Clause)
Federated Learning Framework
Flower is an open-source framework for Federated Learning that provides tools and APIs to simplify the development and deployment of Federated Learning models. Flower is designed to make it easier for developers to implement Federated Learning in their applications by providing a flexible and scalable platform for building and training models.
Flower: A Friendly Federated Learning Framework
Python 2610 Version:v1.4.0 License: Permissive (Apache-2.0)
Kit Solution Source
Federated Learning is a way to train AI models without anyone seeing or touching your data. It seems to have a lot of potentials. Not only it secures user sensitive information, but also aggregates results and identifies common patterns from a lot of users, which makes the model robust, day by day.
Jupyter Notebook 0 Version:v1.0.0 License: Permissive (Apache-2.0)