This project aims to develop a neural expert system to predict credit scores using machine learning algorithms. The system analyzes various features related to credit card usage and financial behavior to determine the creditworthiness of individuals.
The repository contains the following files:
app.py: The main application script that runs the web interface for the credit score prediction system.multiapp.py: A utility script that manages multiple applications within the main web interface.about_us.py: A script that provides information about the team and project.credit_score_prediction.py: The core script that contains the machine learning model and logic for credit score prediction.
Credit history is a record of a borrower's responsible repayment of debts. A credit report is a record of the borrower's credit history from various sources, including banks, credit card companies, collection agencies, and governments.
- Loan Approval: A good credit history increases your chances of loan approval.
- Interest Rates: Better credit history can help you secure lower interest rates.
- Credit Limits: Lenders may offer higher credit limits.
- Payment History: Timely payment of bills.
- Credit Utilization: The ratio of your current debt to your credit limit.
- Length of Credit History: Longer credit history can positively impact your score.
- New Credit: Recent inquiries and newly opened accounts.
- Credit Mix: Types of credit accounts you have.
Understanding these factors can help you maintain a healthy credit history and improve your credit score.
To run this project locally, follow these steps:
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Clone the repository:
git clone https://github.com/Demon-2-Angel/Neural-Expert-System-for-Credit-Card-Prediction.git cd Neural-Expert-System-for-Credit-Card-Prediction -
Create a virtual environment:
python3 -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
This will start the web application on a local server. Open your web browser and navigate to the provided URL to interact with the credit score prediction system.
This script initializes and runs the Streamlit web application, integrating various modules to provide a user-friendly interface for credit score prediction.
This script provides functionality to manage multiple applications within the main Streamlit interface. It allows for the seamless integration of different components of the project.
This script displays information about the project team and the objectives of the credit score prediction system. It serves as an informational page within the web application.
This script contains the machine learning model and prediction logic. It processes user input, applies the trained model, and returns a predicted credit score.
We aim to democratize access to financial insights and empower users with the tools they need to navigate the complexities of credit and lending. Our goals include:
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Enhancing Financial Literacy
- Offering educational resources and tools to help users understand credit scoring and loan processes.
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Promoting Financial Inclusion
- Providing fair and unbiased credit assessments to ensure everyone has access to credit opportunities.
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Supporting Financial Planning
- Enabling users to make well-informed financial decisions with reliable data and predictive insights.
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Innovating Continuously
- Staying at the forefront of technology by continuously improving our algorithms and models to deliver the best possible service.
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Cutting-Edge Technology
- We use the latest AI and ML algorithms to deliver precise and reliable results.
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User-Centric Approach
- Our tools are designed to be user-friendly and easy to understand.
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Transparency and Trust
- We believe in transparent processes and aim to build trust through accuracy and integrity.
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Comprehensive Support
- Our dedicated team is here to provide excellent support and guidance at every step.
- Deekshit
- Aniruddha
- Sanika
Contributions are welcome! Please fork the repository and create a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or suggestions, please contact [foraniruddhakumar@gmail.com].
Feel free to replace [foraniruddhakumar@gmail.com] with your actual contact email or other relevant contact information.
- Install dependencies:
pip install -r requirements.txt
To start the application, run the following command:
streamlit run app.py