2025年美赛C题,建模与数据分析代码集合,涵盖数据清洗、特征工程、奖牌预测、进步退步分析、可视化等多项内容,旨在为奥运奖牌预测与相关因素分析提供完整的解决方案 (源码+文档+论文+数据),开箱即用

上传者: m0_37302966 | 上传时间: 2026-03-23 14:10:02 | 文件大小: 25.01MB | 文件类型: ZIP
目录结构 2025_MCM_Problem_C.pdf / 2025_MCM_Problem_C_cn.pdf:赛题英文与中文原文 2025_Problem_C_Data/:官方原始数据集 summerOly_athletes.csv:运动员信息 summerOly_medal_counts.csv:奖牌统计 其他辅助数据 M23 2025美赛C题1-5问M奖级可运行代码展示+建模教程+结果分析等!2025美赛C题超详细解析教程/:主代码与教程 M23配套资料.../:分模块 Python 脚本 1-1奖牌预测.py:奖牌预测主模型 2-1进步退步分析.py:国家奖牌进步/退步分析 3-1零奖牌统计.py:零奖牌国家统计 3-2奖牌突破概率分析.py:奖牌突破概率分析 4-2.项目设置与奖牌数的关系.py:项目设置与奖牌数关系分析 4-5东道主效应.py:东道主效应分析 其他脚本详见目录 cleaned_data/:数据清洗与中间结果 data_clean.py:数据清洗脚本 grouped_data.csv 等:清洗后数据 预测/:预测相关数据与脚本 predicate.py:预测主脚本 medals_data.csv 等:预测用数据 论文/:相关论文与文档 其他:辅助文件、可视化、报告等 主要功能 数据清洗与预处理:对原始奥运数据进行清洗、归一化、特征工程等处理。 奖牌预测模型:基于线性回归、随机森林等方法,预测 2028 年洛杉矶奥运会各国奖牌数。 进步/退步分析:分析各国奖牌数的历史趋势,识别进步与退步国家。 可视化分析:对奖牌分布、进步退步、东道主效应等进行可视化展示。 辅助分析脚本:如零奖牌统计、项目设置影响、教练效应等。 依赖环境 Python 3.7+ pandas numpy scikit-learn matplotlib seaborn

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