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:

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