CreditGuard

CreditGuard is a machine learning project aimed at credit risk analysis using various classification models. This repository contains a Jupyter Notebook (main.ipynb) that demonstrates the implementation, evaluation, and comparison of different machine learning models, including XGBoost, Decision Tree, Random Forest, and Logistic Regression.

Project Overview

XGBoost Model

Implementation: The notebook includes an XGBoost model for classification, with a focus on hyperparameter tuning.
Hyperparameters Tested: The model iteratively tests combinations of hyperparameters such as colsample_bytree and learning_rate.
Outputs: Training and testing accuracy are recorded for each hyperparameter combination to fine-tune the model for better performance.

Performance Comparison

Models Compared: XGBoost is compared with Decision Tree and Random Forest models.
Findings: XGBoost generally outperforms the other models, with suggestions for further improvements through feature engineering and scaling.

Logistic Regression

Implementation: A Logistic Regression model is also included for comparison.
Note: The model's accuracy is reported, along with a convergence warning indicating that the maximum number of iterations was reached.

Model Evaluation

Metrics: The notebook evaluates models using various metrics such as accuracy, recall, precision, and F1-score.
Significance: Each metric is computed and discussed to understand the performance and reliability of the models.

Further Analysis

Future Work: Recommendations for further enhancement include additional feature engineering and scaling to optimize the performance of the XGBoost model.

Getting Started

To get started with this project, follow these steps:

Clone the Repository:

git clone https://github.com/yourusername/CreditGuard.git
cd CreditGuard

Dependencies

Python 3.x
Jupyter Notebook
XGBoost
scikit-learn
pandas
numpy

Ensure you have the above dependencies installed. You can find the full list of required packages in requirements.txt.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

XGBoost: For its efficient and scalable implementation of gradient boosting.
scikit-learn: For providing a range of machine learning algorithms and evaluation metrics.

anubhav1433/CreditGuard