Welcome to the data-driven journey through the Lending Club universe! 🚀 Here, we slice and dice the numbers to predict loan defaults, harnessing the power of machine learning and neural network wizardry.
data/: The treasure chest where the dataset gems are stored. You might find this chest empty due to GitHub's size limits, but fear not! The map to the data treasure is provided below. 🗺️lgb_Lending_Club.ipynb: The spellbook 📜 where the magic happens - dive into this Jupyter notebook for an enchanting code experience with LightGBM and more!README.md: You are here! 📍 This scroll details everything you need to embark on your data quest.
Embark on a two-part adventure:
- Part 1: We craft a baseline model using the mystic arts of LightGBM, conjuring derived variables to enhance our model’s foresight. 🔮
- Part 2: We ascend to the data analysis zenith by melding machine learning alchemy with deep learning sorcery for an even mightier model. 🧙
基于Lending Club的数据分析实战项目【小白记录向】【二】
[English Version]Data Analysis Practical Project Based on Lending Club[2] Every step of this epic tale unfolds in the magical land of Google Colab.
To launch your quest:
- Clone this repository to your local machine.
- Seek the dataset from the Lending Club dataset URL provided and let it rest in the
data/sanctuary. - Unveil the
lgb_Lending_Club.ipynbnotebook in Jupyter to behold the analysis.
Before you begin, arm yourself with:
- Python 3.x
- Jupyter Notebook or JupyterLab
Join forces with us! Your contributions are the lifeblood of this project's evolution. Fork the repository, enhance it with your spells, and issue a pull request back to the main branch.
This project is bound by the MIT License, ensuring that knowledge is shared freely among all apprentices.