In this project, We try to predict Adult Census Income Dataset by using XGBoost Gradient Boosted Trees System.
This directory contains 3 main programs:
-
AutogluonProcess.ipynb&AutogluonProcess.py: These files are used to run Autogluon and help us decide which model to use. -
XGBoost.ipynb: This file is used to run XGBoost. -
Visualization.ipynb: This file is for data visualization.
Needed Python Version:
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
python <=3.11 (package autogluon can't support python 3.12!)
! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
My computer runs well in 3.11
Needed Package:
The following version work well in Anaconda:
| Library | Version | Notes |
|---|---|---|
| autogluon | 1.1.0 | 自动化机器学习包 |
| pandas | 2.2.1 | 处理数据 |
| warnings | in Python | - |
| matplotlib | 3.8.4 | 画图 |
| seaborn | 0.12.0 | 数据可视化 |
| sklearn | 1.4.0 | 决策树框架 (1.5.0 tested OK) |
| xgboost | 2.0.3 | 提供 XGBoost 分类器 |
| jupyter | 1.0.0 |
XGBoost: eXtreme Gradient Boosting library. Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
Installing
Pip 21.3+ is required
pip install xgboost
Installing autogluon
(tips: install time is a bit long! And we don't fully need it!)
这个包有很多版本限制，它可能会改掉一些下载的东西（比如它会卸载scikit-learn 1.4.2，然后重新安装个版本）要依赖很多包（比如torch），文件非常大，并且需要管理员权限来安装，我们只在Autogluon文件中进行了跑， 并且最终提交没有依赖那两个文件，而是XGBoost文件，如果跑不起来也没问题。 另外很神奇的是，这个包的预测跟它的版本有关
pip install autogluon
This project's Github website: https://github.com/Laihb1106205841/CS311-AI-Peoject3-XGBoost.git
XGBoost Introduction: https://xgboost.readthedocs.io/en/stable/tutorials/model.html
XGBoost Github website: https://github.com/dmlc/xgboost?tab=security-ov-file