Binary Classification of Insurance Cross Selling

A collection of Jupyter notebooks and scripts for kaggle competition Binary Classification of Insurance Cross Selling.

Description

This repository contains various experiments using different machine learning models and techniques, including:

• AutoGluon
• CatBoost with Hyperopt
• XGBoost
• LightAutoML
• Dense layers
• Generalization Gamble

Each notebook explores different aspects and implementations of these models.

Installation

To run these notebooks, you need to have Python installed along with the required libraries. You can install the dependencies using pip.

pip install -r requirements.txt

Usage

Clone the repository and navigate to the directory:

git clone https://github.com/NZHGREALISH/kaggle_grealish.git
cd kaggle_grealish

Then, open the desired Jupyter notebook using JupyterLab or Jupyter Notebook:

jupyter notebook notebook_name.ipynb

Notebooks

• Autogluon.ipynb: Example using AutoGluon for model training and evaluation.
• Catboost-with-Hyperopt得分0.89610.ipynb: Using CatBoost with Hyperopt for hyperparameter tuning.
• GeneralizationGamble.ipynb: Exploring generalization techniques.
• xgboost_ryan_gpu.ipynb: Implementation of XGBoost with GPU acceleration.
• lightautoml.ipynb: Experiment with LightAutoML for model training.

Contributing

Contributions are welcome! Please fork this repository and submit pull requests with your improvements.

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Open a pull request

Authors

• Ryan-Ren0330
• NZHGREALISH

License

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