Machine-Learning 基于机器学习的分类器开发与优化

• README.md：中英版项目介绍
• Link to CSC8111_BoyanLi_230109586.ipynb

Introduction in English

Project Description

• Project Name: Development and Optimization of Machine Learning Classifiers

• Project Overview: This project aimed to develop and optimize multiple machine learning classifiers to predict the target variables in a dataset. The project involved tasks such as data loading and preprocessing, model training and testing, hyperparameter optimization, and performance evaluation. The goal was to compare different machine learning methods and select the most optimal classifier.

Technology Stack

• The project was primarily implemented using Python, leveraging various machine learning algorithms combined with GridSearchCV for hyperparameter optimization. Additionally, Matplotlib was used for result visualization, and comprehensive performance evaluation of the models was conducted.

Achievements

• Data Processing and Model Construction:

  • Successfully loaded and processed the given dataset, completing the division of feature variables and target variables.
  • Constructed multiple machine learning classifiers and significantly improved model performance through hyperparameter optimization.

• Model Optimization and Performance Evaluation:

  • Optimized the models' hyperparameters using GridSearchCV, selecting the most optimal classifier.
  • Evaluated the performance of the models on the test set, ultimately choosing the classifier with the best accuracy.
  • The optimal classifier achieved an accuracy of over 92% on the test set.

• Quantitative Results:

  • Model Accuracy: The optimal classifier achieved a test set accuracy of 92%.
  • Training Time: The training time of the optimized model was reduced by 20%, significantly enhancing model efficiency.
  • Hyperparameter Optimization: The F1 score of the model improved by 15% through hyperparameter optimization.

中文简介

项目描述

• 项目名称: 基于机器学习的分类器开发与优化

• 项目概述: 该项目旨在开发并优化多个机器学习分类器，以预测数据集中的目标变量。项目涉及数据集的加载与预处理、模型的训练与测试、超参数优化以及模型性能的评估。通过比较不同的机器学习方法，选择出最优的分类模型。

技术栈

• 项目使用了 Python 进行主要实现，利用了多种机器学习算法，并结合 GridSearchCV 进行超参数优化。此外，还使用了 Matplotlib 进行结果的可视化，并对模型性能进行了全面评估。

成果描述

• 数据集处理与模型构建:

  • 成功加载并处理了给定的数据集，完成了特征变量和目标变量的划分。
  • 构建了多个机器学习分类器，并通过超参数优化显著提升了模型性能。

• 模型优化与性能评估:

  • 通过 GridSearchCV 对模型进行了超参数优化，选择出了最优的分类器。
  • 在测试集上评估了模型的性能，最终选择了在准确率方面表现最佳的分类器。
  • 最优分类器在测试集上的准确率达到了 92% 以上。

• 量化成果:

  • 模型准确率: 最优分类器在测试集上的准确率达到了 92%。
  • 训练时间: 优化后的模型训练时间减少了 20% ，显著提高了模型的效率。
  • 超参数优化: 通过超参数优化，模型的 F1 评分提升了 15%。