talivia-group
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talivia
Talivia is an open-source, self-hosted analytics platform that combines web analytics, session replay, and customer revenue tracking from multiple payment providers.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它提供了一种清晰、归一化的方法来比较各大洲的数学奥林匹克成绩，是数据科学应用于竞赛分析的实践示例。
适合谁使用
- 寻求实践分析项目的数据科学学习者
- 数学竞赛组织者与统计人员
- 关注区域STEM表现的教育工作者
- 比较各国成绩的奥林匹克爱好者
典型使用场景
- 评估各大洲在数学奥林匹克训练中的相对优势
- 课堂演示数据可视化与归一化技术
- 扩展分析以包含多年趋势或其他指标
- 快速参考以便针对特定大洲分配提升资源
项目优势
- 简单的加权计分系统（金牌5分、银牌3分、铜牌1分、荣誉提名0分），易于理解
- 按国家归一化，消除了各大洲参赛国家数量不等的影响
- 两个清晰的图表（饼图与热力图）提供直观洞察
- 运行门槛低，仅需Python及pandas、matplotlib、seaborn库
使用前须知
- 仅基于2026年数据，无法反映年度趋势或波动
- 加权分值具有任意性，未必反映实际竞赛竞争态势
- 分析仅停留在洲际层面，未提供国别细分
- README未描述数据集（imo_2026_medals.csv）的来源或准确性验证
README 快速开始
🏆 IMO 2026 - Regional Performance Analysis 🌍 | International Mathematical Olympiad Data Science
Welcome to the IMO 2026 Regional Performance Analysis repository! This open-source data science project provides a comprehensive statistical analysis of the relative performance of different continents at the International Mathematical Olympiad (IMO) 2026. 📊
Leveraging Python, Pandas, and Data Visualization techniques, this project analyzes the provided CSV dataset containing the global mathematical competition medal counts (Gold 🥇, Silver 🥈, Bronze 🥉) and Honorable Mentions 🎖️ for each participating country.
🧮 Scoring Methodology & Data Analytics
To fairly compare continents, this analysis uses a weighted points system to assess overall performance:
- 🥇 Gold Medal: 5 points
- 🥈 Silver Medal: 3 points
- 🥉 Bronze Medal: 1 point
- 🎖️ Honorable Mention: 0 points
Additionally, because the number of participating countries varies widely by continent, the metrics are normalized by the number of countries in each continent. 📈 This results in a "per-country average" for both medal counts and the overall weighted score, giving a much more accurate representation of relative regional strength. 🧠
📈 Visualizations
The analysis generates two primary visualizations:
- 🥧 Pie Chart: Shows the distribution of the average weighted score per country across continents.
- 🗺️ Heatmap: Displays the average number of each award type (Gold, Silver, Bronze, HM) won per country for every continent.
🚀 How to Run
🛠️ Prerequisites
You need Python 🐍 installed along with the pandas, matplotlib, and seaborn libraries.
You can install the dependencies via pip:
pip install pandas matplotlib seaborn
💻 Execution
Run the analysis script to generate the updated plot:
python analyze.py
This will read from imo_2026_medals.csv 📁 and output/overwrite the assets/imo_2026_continent_performance.png 🖼️ image.
Star History
项目描述
IMO-Hot-Regions
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