基于统计机器学习的 DOTA2 阵容的最优抉择 The optimal choice of DOTA2 lineup based on statistical machine learning 内容摘要论文首先详细介绍了 DOTA2 以及统计机器学习的背景以及其的现状，然后讨论了用统计机器学习方法探索 DOTA2 最佳阵容的设计目标，项目要求和整体项目设计，以及展开项目的具体设计和开发的全面讨论。本文项目功能基于 Python 的 Beautiful Soup、Selenium、Matplotlib等库，编写对应代码而实现了一个 DOTA2 最佳阵容抉择的项目。项目包括爬虫、统计机器学习等重要功能。关键词：Python、爬虫、数据处理、统计机器学习AbstractThe paper firstly introduces the background and current situation of DOTA2 and statistical machine learning in detail, then discusses the design objectives, project requirements and overall project design of DOTA2 best team by statistical machine learning method, as well as a comprehensive discussion on the specific design and development of the project.Based on Python's Beautiful Soup, Selenium, Matplotlib and other libraries, this article implements a best-team choice for DOTA2 by writing corresponding code. The project includes important functions such as crawler and statistical machine learning.Key words: Python, crawler, data processing, statistical machine learning目 录第一章 绪论........................................................................................................................11.1 课题背景及意义....................................................................................................11.2 国内外研究现状....................................................................................................21.3 课题研究内容........................................................................................................3第二章 开发技术与工具....................................................................................................42.1 Python 简介.................................................
