This project aims to develop a machine learning model for predicting credit card approval using the Credit Card Approval dataset from the UCI Machine Learning Repository. The goal is to automate the credit card application analysis process, which is currently mundane, error-prone, and time-consuming for commercial banks.
- Introduction
- Dataset
- Installation
- Preprocessing
- Exploratory Data Analysis
- Model Development
- Evaluation
- Usage
- Contributing
Commercial banks receive numerous credit card applications, many of which are rejected due to various reasons such as high loan balances, low-income levels, or multiple inquiries on an individual's credit report. This project demonstrates the use of machine learning techniques to build an automatic credit card approval predictor, mimicking the practices adopted by real banks.
The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository. It contains a combination of numerical and non-numerical features, representing attributes typically found in credit card applications. The features include Gender, Age, Debt, Marital Status, Education Level, Ethnicity, Years Employed, Prior Default, Employment Status, Credit Score, Driver's License, Citizenship, Zip Code, Income, and Approval Status.
To run the project, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/credit-card-approval-predictor.git
cd credit-card-approval-predictor
- Create a virtual environment (optional):
python3 -m venv venv
source venv/bin/activate
- Install the required packages:
pip install -r requirements.txt
The dataset requires preprocessing to ensure the machine learning model can make accurate predictions. This involves handling missing entries, converting non-numerical features to numerical representations, and normalizing the values from different ranges.
Before building the model, exploratory data analysis is performed to gain insights into the dataset. This step helps build intuition and identify patterns or correlations that can aid in model development.
The machine learning model is developed using appropriate credit card approval prediction techniques. Various algorithms are explored and evaluated to determine the best-performing model.
The model's performance is assessed using suitable evaluation metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques may also be employed to ensure the model's robustness.
Once the project is set up and the model is trained, the credit card approval predictor can be used in real-world scenarios. It takes as input the relevant applicant information and predicts whether the application will be approved or rejected.
Contributions to the project are welcome. If you have any suggestions, bug reports, or feature requests, please open an issue on the project's GitHub repository.