Goal of this analysis

The goal of this analysis is to :

1. Build an interactive dashboard to explore each country’s Olympic performance from 1976 to 2008 using time series analysis and SQL.
2. Identify and compare medal-winning trends of up-and-coming countries and historically dominant nations.
3. Use SQL to uncover patterns in sports performance and regional medal dominance, visualized in an interactive Streamlit dashboard.
4. Utilizing SQL to uncover patterns in sports performance and medal domincance according to specific

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Deliverables

SQL-Powered Interactive Dashboard (via Streamlit):

• Country-level medal trends (1976–2008)
• Group-wise (Western vs. Emerging)
• Dynamic filtering by sport, medal type and gender

Tools Used:

• SQLite database for structured Olympic data
• SQL for data query filteration
• Streamlit for real-time user interaction

Core Analysis:

• Grouped countries by development level (Western vs. Emerging)
• Time-based comparisons using SQL GROUP BY logic to compare performances
• Aggregated medal counts by year, sport, and region

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Project Objectives

SQL-Powered Interactive Dashboard (via Streamlit):

• Explore medal trends from 1976-2008
• Compare performances between Western and Emerging nations
• Filter variables such as medal type sport and gender for deeper exploration between data and variable types.

This project demostrates how SQL and Streamlit can be integrated to produce real time data , with the combination of python based data manipulation and SQL.

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What data do I have?

• Source:
  • Kaggle – Summer Olympic Medals Dataset
• Data Characteristics:
  • Structured .csv converted to .db using Python and sqlite3
  • ~15,000+ medal records from 1976–2008
  • Fields include: Year, Country, Sport, Discipline, Event, Gender, Medal
• Cleaning Steps:
  • Dropped nulls in key fields (year, country, medal)
  • Standardized country and medal labels
  • Renamed columns for SQL compatibility </aside>

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SQL Query Evaluation

• SQL queries powered all dashboard logic — no Python data manipulation
• Used GROUP BY, CASE, HAVING, and conditional filters
• Example query used to compare Western vs. Emerging nations: </aside>

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Conclusion

This project delivered a fully SQL-driven interactive dashboard analyzing Olympic medal performance from 1976 to 2008.

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python3 -m venv .venv
source .venv/bin/activate
pip install streamlit
streamlit run dashboard.py

https://www.kaggle.com/datasets/divyansh22/summer-olympics-medals