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FR-AI / ANALYSISWhy it is worth attention
It offers a clear, normalized methodology for comparing mathematical Olympiad performance across continents, making it a practical example of data science applied to competition analytics.
Who it is for
- Data science learners seeking a hands-on analytics project
- Math competition organizers and statisticians
- Educators interested in regional STEM performance patterns
- Olympiad enthusiasts comparing country-level achievements
Use cases
- Benchmarking regional strengths in mathematical Olympiad training
- Classroom demonstration of data visualization and normalization techniques
- Extending the analysis to include multi-year trends or additional metrics
- Quick reference for allocating resources to improve specific continent performance
Strengths
- Simple weighted scoring system (Gold=5, Silver=3, Bronze=1, HM=0) that is easy to understand
- Normalization per country accounts for uneven participation across continents
- Two clear visualizations (pie chart and heatmap) provide immediate insight
- Low barrier to run – only requires Python with pandas, matplotlib, and seaborn
Considerations
- Based on a single year (2026) – trends or year‑to‑year variability are not captured
- Weighted point values are arbitrary and may not reflect actual competition dynamics
- Analysis is at continent level only; no granular country‑by‑country breakdown is provided
- The dataset (imo_2026_medals.csv) is not described or validated for accuracy in the README
README quick start
🏆 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.
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Description
IMO-Hot-Regions
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