This project focuses on analyzing Olympic Games data to explore patterns, trends, and relationships between various factors such as government investments in sports and medal counts. By leveraging Python and its data analysis libraries, the project aims to provide insights into how countries perform in the Olympics based on their socio-economic factors and investments in sports. The present analysis comes from poor Portugal performance in the Olympic Games. I want to understand and compare Nations to observe any signific points. I come to the conclusion that despite Countries having different number of habitants, an increase in expenditure in Sports will increase it's results/performance

• Data Cleaning and Transformation: Handling missing values, filtering, and restructuring the dataset for analysis.
• Descriptive Statistics: Exploring distributions of numerical variables like age, weight, and height of athletes.
• Hypothesis Testing: Conducting t-tests to identify significant differences (e.g., comparing male and female athletes’ heights or weights).
• Medal Analysis: Filtering data to explore relationships between medals won and other variables like investment.
• Investment Analysis: Comparing government expenditures on recreational sporting services with medal counts to assess correlations.
• Exporting Results: Saving insights into Excel files for sharing and further use.

• Programming Language: Python
• Libraries:
  • pandas for data manipulation
  • numpy for numerical computations
  • scipy for statistical tests
  • matplotlib and seaborn for data visualization
• Tools: Jupyter Notebook for development and analysis

• The dataset includes:
  • Athlete details: Age, sex, height, weight, and performance.
  • Event details: Sport, year, and city.
  • Medal counts: Gold, Silver, Bronze, and NA.
• Additional data on government investments in recreational sporting services (external sources).

• https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results/data?select=athlete_events.csv

• Medal Trends: Countries with higher investments in sports often perform better in the Olympics.
• Athlete Characteristics: Average height and weight vary significantly across sports and genders.
• Government Expenditures: Strong correlation between medal counts and sports funding.

• Defined project goals and scope.
• Collected and explored datasets related to Olympic medals and sports investment.
• Conducted initial exploratory data analysis (EDA) to understand the data structure.

• Cleaned and preprocessed data (e.g., handling missing values, formatting inconsistencies).
• Performed statistical tests (e.g., T-tests) to explore correlations between variables like age, year, and medals.
• Developed Python scripts to filter, sort, and analyze key data points.
• Began researching government expenditure on recreational sports.

• Merged Olympic data with external data on government sports expenditure.
• Visualized relationships between investment and medal counts (e.g., charts, graphs).
• Finalized and documented key findings and insights.
• Drafted the project's README file and prepared data for sharing.