Data Visualization – Olympic Athletes Analysis This project explores and visualizes data related to Olympic athletes, their events, and the medals they've won. Utilizing Python libraries such as Matplotlib, Pandas, and Seaborn, the analysis provides insights into various aspects of the Olympic Games.

📁 Repository Contents olympics_athletes_analysis.ipynb: A Jupyter Notebook containing the data analysis and visualizations.

athlete_events.csv: The dataset used for analysis, detailing athlete information, events, and medal outcomes.

📊 Features Data Cleaning: Handling missing values and preparing the dataset for analysis.

Exploratory Data Analysis (EDA): Investigating patterns and trends within the data.

Visualizations: Distribution of athletes across different sports and countries. Medal counts by country and year. Gender participation trends over time. Age distribution of medal-winning athletes.

🛠️ Technologies Used Python: Programming language for data analysis.

Pandas: Data manipulation and analysis.

Matplotlib: Creating static, animated, and interactive visualizations.

Seaborn: Statistical data visualization based on Matplotlib.

Jupyter Notebook: Interactive computing environment for developing and presenting data analysis.

📈 Getting Started Clone the repository: git clone https://github.com/V1629/Data_Visualization.git Navigate to the project directory:

cd Data_Visualization

Install the required libraries: pip install pandas matplotlib seaborn

Open the Jupyter Notebook: jupyter notebook olympics_athletes_analysis.ipynb

📄 Dataset Source The dataset used in this project is publicly available and contains detailed information about Olympic athletes, their events, and medals won. It can be found at Kaggle - Olympic History Dataset.