Credit-Score-Model

Credit Scoring Classification

This project aims to build a classification model for credit scoring using various machine learning algorithms. The goal is to predict whether a customer is a good or bad credit risk based on their financial and demographic information.

Dataset

The dataset used in this project is not provided. However, it should contain relevant features such as income, employment status, credit history, and other factors that may influence credit risk.

Dependencies

The following Python libraries are required to run this project:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost
• CatBoost

Preprocessing

The preprocessing steps include:

1. Handling missing values using SimpleImputer.
2. Standardizing numerical features using StandardScaler.
3. Encoding categorical features using LabelEncoder.

Model Building

The following classification algorithms are used in this project:

• Random Forest Classifier
• XGBoost Classifier
• CatBoost Classifier

Evaluation

The models are evaluated using the following metrics:

• Confusion Matrix
• Classification Report

The confusion_matrix and classification_report functions from Scikit-learn are used to calculate these metrics.

Usage

1. Clone the repository or download the project files.
2. Ensure that all the required dependencies are installed.
3. Prepare your dataset and place it in the appropriate location.
4. Update the code with the correct path to your dataset.
5. Run the script to preprocess the data, train the models, and evaluate their performance.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.