This project develops a predictive model for credit card approval using Python and machine learning techniques. The aim is to predict whether a credit card application should be approved based on the applicant's financial data. This model leverages logistic regression, random forest, XGBoost, and neural networks to analyze and predict outcomes.
To set up and run this project, follow these steps:
- Clone the repository:
git clone https://github.com/Antonio-IF/Project3CreditModels.git
- Create and activate a virtual environment:
-
For Windows:
python -m venv venv venv\Scripts\activate -
For macOS/Linux:
python3 -m venv venv source venv/bin/activate
- Install the necessary dependencies:
pip install -r requirements.txt
To execute the model and see the predictions, follow these steps:
- Navigate to the project directory:
cd Project3CreditModels
- Run the main script:
main.py
More details about the code structure and functionalities are available in the source code comments. Each script within the project is thoroughly documented to explain the functionalities of each function and how data is utilized.
This project was developed by Antonio-IF along with collaborators such as anasofiabrizuela, diegotita4, luisrc44, and Oscar148.
This project is licensed under the MIT License - see the LICENSE.md file for more details.
This project is currently in development. Future updates will include enhancements in model accuracy and user interface improvements for easier deployment in commercial applications.
For more information, contact:
- Antonio-IF at the following email: if728370@iteso.mx
- anasofiabrizuela at the following email: ana.brizuela@iteso.mx
- diegotita4 at the following email: if728356@iteso.mx
- luisrc44 at the following email: luis.robles@iteso.mx
- Oscar148 at the following email: oscar.alvarado@iteso.mx