Welcome to the Olympics Data Analysis Project! This project was inspired by the excitement and global unity brought about by the ongoing Olympics. As a passionate data enthusiast, I sought to channel my skills into a project that not only celebrates the spirit of the Olympics but also showcases my capabilities in data analysis, visualization, and storytelling.
Streamlit app link: https://itsdivya-olympics-data-analysis.streamlit.app/
The Olympics is a grand event that brings together athletes from around the world, showcasing their dedication, perseverance, and excellence. This project dives deep into the historical and contemporary data of the Olympics to uncover trends, patterns, and insights. The aim is to provide a comprehensive analysis that not only highlights the achievements of athletes but also reflects the evolution of the Olympics over time.
As the world tuned in to watch the 2024 Paris Olympics, I felt a surge of inspiration to create something meaningful and relevant. This project serves as a testament to my passion for data science and my ability to transform raw data into compelling stories. It's an opportunity to demonstrate my proficiency in various data science tools and techniques, all while celebrating one of the most iconic global events. I came across this wonderful video tutorial by CampusX and decided to create my own Olympics Data Analysis Project.
The data for this project was sourced from publicly available datasets on Olympic games, including historical records of athletes, events, and medals. The primary datasets used include:
This project includes several key features:
- Data Cleaning and Preprocessing: Handling missing values, transforming the data into a usable format and combining data from different tables.
- Descriptive Statistics: Created detailed pivote tables and aggregates to describe the statistics of each sports.
- Trend Analysis: Analyzing trends in the number of events, participating nations, and medal counts over time.
- Country-wise Performance: Comparing the performance of different countries in various sports across multiple Olympic games.
- Athlete Analysis: Exploring data on individual athletes, including age, gender, and event participation.
- Interactive Visualizations: Creating compelling visualizations to illustrate key findings.
The analysis is structured to cover multiple aspects of the Olympics:
- Historical Trends: Examining the growth of the Olympics in terms of participating countries, events, and athletes.
- Top Performing Nations: Identifying nations with the highest medal counts and understanding the factors contributing to their success.
- Athlete Performance: Delving into the demographics and performance metrics of athletes, including age, gender, and event participation.
- Sport-Specific Analysis: Focusing on specific sports to understand the dynamics and trends within each discipline.
- Python: The primary programming language used for data analysis.
- Pandas: For data manipulation and analysis.
- Matplotlib & Seaborn: For creating static visualizations.
- Plotly: For creating interactive visualizations.
- Jupyter Notebook: For documenting and presenting the analysis.
- Streamlit: For creating an interactive web application to showcase the analysis (if applicable).
- Clone the Repository:
git clone https://github.com/yourusername/olympics-data-analysis.git
- Install Dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook:
Open the notebook file to explore the analysis step by step.
jupyter notebook Olympics.ipynb.ipynb jupyter notebook Overall_analysis.ipynb jupyter notebook Athletes_analysis.ipynb jupyter notebook Country_analysis
- Deploy the Streamlit App (if applicable):
streamlit run app.py
- Expand the Dataset: Incorporating more data from recent Olympic games to keep the analysis up to date. The data for the ongoing Olympics has not been added.
- Advanced Machine Learning Models: Applying predictive models to forecast medal counts or athlete performance in future games.
- Enhanced Visualizations: Creating more sophisticated and interactive visualizations to further enhance the storytelling aspect.
Contributions are welcome! If you have ideas, suggestions, or improvements, feel free to open an issue or submit a pull request. Let’s collaborate to make this project even better.