Predict loan defaults using ML. Leverage Logistic Regression, Random Forest, XGBoost. Preprocess data, train models, analyze features. Make informed lending decisions. Jupyter Notebook and code.
ML Loan Default Predictor is a machine learning project that aims to predict loan defaults using various algorithms such as Logistic Regression, Random Forest, and XGBoost. The project includes data preprocessing, model training, feature analysis, and making informed lending decisions based on the predictions. This repository contains the Jupyter Notebook and code for the project.
To use this project, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/ML-Loan-Default-Predictor.git - Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook file
ML-Loan-Default-Predictor.ipynb. - Run the cells in the notebook to execute the code step by step.
- Follow the instructions in the notebook to preprocess the data, train the models, and analyze the features.
- Make informed lending decisions based on the model predictions.
The project utilizes a dataset containing loan information such as borrower details, loan amount, interest rate, credit score, employment history, etc. The data is provided in a CSV format and should be placed in the data directory of this repository.
After running the notebook, you will obtain predictions for loan defaults using the different models. Analyze the results, including accuracy, precision, recall, and other relevant metrics. Visualize the results using appropriate plots and graphs.
Contributions are welcome! If you want to contribute to this project, please follow these guidelines:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them with descriptive messages.
- Push your changes to your forked repository.
- Submit a pull request explaining your changes.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the terms of the license.
- Robert Rusev - robertrusev
- I would like to thank the contributors of the scikit-learn, XGBoost, and Pandas libraries for their valuable tools and resources.