Olympics Data Analysis (1896 - 2016)

Welcome to the Olympics Data Analysis project! The Olympics Data Analysis Project aims to delve into the rich history of the Olympic Games, this repository contains a comprehensive analysis of the Olympics data spanning from the first modern Olympics in 1896 to the 2016 Games. This project leverages Python to explore and visualize trends, performance, and historical insights from over a century of Olympic history over the time.

Features

• Data Cleaning and Preprocessing: Scripts for cleaning and preparing raw Olympics data for analysis.
• Exploratory Data Analysis (EDA): Jupyter notebooks with detailed EDA to uncover interesting trends and patterns in the data.
• Visualizations: A variety of charts and graphs to visualize athletes' performance, medal distributions, country-wise participation, and more.
• Medal Distribution: Examine how medals have been distributed across different countries and sports over the years.
• Historical Insights: Discover how the Games have evolved, including changes in events, athlete participation, and country representation.
• Performance Analysis: Analyze the performance trends of athletes and countries, identifying factors that contribute to success.
• Gender Analysis: Explore the participation and performance trends of male and female athletes.
• Interactive Dashboards: Interactive visualizations for user-friendly data exploration.

Project Components

1. Data Collection and Preparation:
   • Comprehensive dataset spanning from 1896 to 2016.
   • Data cleaning and preprocessing scripts to ensure accuracy and consistency.
2. Exploratory Data Analysis (EDA):
   • In-depth analysis to identify key trends and interesting patterns.
   • Visualization of various metrics, including athlete performance, medal counts, and participation rates.
3. Visualizations:
   • Static and interactive charts to illustrate findings.
   • Dashboards for dynamic data exploration and analysis.

Technologies Used

• Python: The primary language for analysis and modeling.
• Pandas: For efficient data manipulation and analysis.
• Matplotlib/Seaborn: For static visualizations.
• Plotly: For creating interactive visualizations.
• Jupyter Notebook: For creating and sharing detailed analysis.
• Streamlit: For hosting our web app

Demo Video

olympics-analysis-demo.mp4

Getting Started

To get started with the project, clone this repository and install the necessary dependencies:

git clone https://github.com/dattabiplab/olympics-analysis.git
cd olympics-analysis
pip install -r requirements.txt

Deploy this app Locally

1. Clone this repository and install the necessary dependencies.
2. In any code editor like VS Code or Pycharm run the app.py

   streamlit run app.py
   

3. To see the notebook open the Olympics-analysis.ipynb file in Jupyter-Notebook or any other.