This paper presents a comprehensive analysis of historical Olympic Games data, with a particular focus on trends in athlete participation, gender distribution, medal counts, and sports popularity. Using a dataset from Kaggle that spans over 252,566 athlete records, the study delves into the evolving landscape of the Olympics, highlighting a notable increase in female participation over recent decades a trend aligned with the International Olympic Committee's efforts toward gender equality. Country performance analysis underscores the dominance of nations like the United States, China, and Russia, while also shedding light on emerging contributions from African countries, including South Africa, Kenya, Egypt, and Nigeria. Additionally, this study uses machine learning models to predict participation trends and medal outcomes in future Olympic Games, with a specific focus on forecasting the performance of African nations. The findings offer valuable insights into both historical patterns and potential future developments in global sports, contributing to a deeper understanding of the Olympics’ changing dynamics and aiding policymakers in promoting equitable participation and success.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Predicting African Participation in Olympics: Gender and Sport Type Trends

  • Vaishnavi Patel,
  • Surya Teja Yerrolla,
  • Samah Senbel

摘要

This paper presents a comprehensive analysis of historical Olympic Games data, with a particular focus on trends in athlete participation, gender distribution, medal counts, and sports popularity. Using a dataset from Kaggle that spans over 252,566 athlete records, the study delves into the evolving landscape of the Olympics, highlighting a notable increase in female participation over recent decades a trend aligned with the International Olympic Committee's efforts toward gender equality. Country performance analysis underscores the dominance of nations like the United States, China, and Russia, while also shedding light on emerging contributions from African countries, including South Africa, Kenya, Egypt, and Nigeria. Additionally, this study uses machine learning models to predict participation trends and medal outcomes in future Olympic Games, with a specific focus on forecasting the performance of African nations. The findings offer valuable insights into both historical patterns and potential future developments in global sports, contributing to a deeper understanding of the Olympics’ changing dynamics and aiding policymakers in promoting equitable participation and success.