Machine Learning-Based Credit Scoring System (MLCSS)

Description

Machine Learning-Based Credit Scoring System (MLCSS) is a machine learning algorithm designed to evaluate and score the creditworthiness of individuals.

Offering

This project is available for purchase. For inquiries regarding pricing and licensing, please contact us at quantascript@gmail.com.

Mathematical Equations

1. Logistic Regression: Predicting probability of default

   P ( y = 1 | X ) = σ ( X · β )

2. Random Forest: Aggregating decision trees for classification

   y ^ = 1 N ∑ i=1 N f i ( X )

Installation

To use MLCSS, you'll need to install the following dependencies:

• numpy
• pandas
• scikit-learn

You can install them using pip:

pip install numpy pandas scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/QuantaScriptor/Machine-Learning-Based-Credit-Scoring-System-MLCSS.git

2. Navigate to the project directory:

   cd Machine-Learning-Based-Credit-Scoring-System-MLCSS

3. Run the script:

   python mlcss.py

License

This project is licensed under the GNU Affero General Public License v3.0. See the LICENSE file for details.