This project aims to predict financial risk for individuals based on various factors and features like City, Location_score, Internal_Audit_Score, External_Audit_Score, Fin_Score, Loss_Score, and past results. By utilizing machine learning techniques like Classification, we can make informed predictions about a person's creditworthiness and financial stability.
- Data preprocessing and feature engineering
- Model training and evaluation
- Risk prediction using trained models
- Web interface for interacting with the model. Streamlit
Once the Streamlit application is running, you will be presented with a user interface containing input fields for various features. Enter the relevant details, such as the City, Location_score, Internal_Audit_Score, External_Audit_Score, Fin_Score, Loss_Score, and past results then click on the "Predict" button. The application will utilize the trained Logistic Regression model to generate the prediction for the person based on the provided features.
Contributions to this project are welcome. If you would like to contribute, please follow these steps:
- 1)Create a new branch from the
mainbranch to work on your changes. - 2)Make your modifications and commit your changes.
- 3)Push your branch to your forked repository.
- 4)Open a pull request to the original repository, describing the changes you made.
This project is licensed under the GPU License.
- The dataset used in this project is sourced from: machinehack.com.
- The Logistic Regression algorithm is implemented using the scikit-learn library.
- The Streamlit framework is used for creating the web application.
If you have any questions or suggestions regarding this project, please feel free to contact me at 132anaskhan@gmail.com.