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120 years of Olympics Analysis

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5 interesting questions answered using descriptive statistics

  1. What is the median height/weight of an Olympic athlete? Height 175.0cm, Weight 70.0kg

  2. What is the median height/weight of an Olympic medalist? Height 178.0cm, Weight 73.0kg

  3. What is the best age to win a golden medal? Answer: 24

  4. Which countries have won the most medals? Overall and per year? Answer:

Year

Country

Medals

1980

URS

496

1904

USA

394

1908

GBR

368

1988

URS

366

1984

USA

361

5. Which year was the most competitive Olympics? 2016 with 11174 medals won


 

  • Data Acquisition:

    • Python libraries like pandas for data handling and matplotlib/seaborn for visualization.

    • CSV file located below:


  • Data Preparation:

    • Descriptive Statistics: The dataset was first explored using descriptive statistics, which provided information such as the mean, standard deviation, and count for columns like Age, Height, and Weight.

    • The dataset was cleaned or filtered, although specific steps like handling missing values or data normalization aren't detailed in the initial cells.


  • Data Analytics:

    • Athlete Characteristics: The notebook addresses questions such as the median height/weight of an Olympic athlete and Olympic medalists. It also explores the best age to win a gold medal.

    • Country Performance: Analysis was conducted to determine which countries have won the most medals, both overall and per year.


  • Data Visualization:

    • While the specific plots aren't listed, the use of matplotlib and seaborn indicates that the notebook likely includes visualizations like histograms, bar charts, or scatter plots to represent the data analysis results visually.


  • What Else Could Have Been Done?:

    • Time Series Analysis: Explore trends in athlete performance or medal counts over time.

    • Predictive Modeling: Develop models to predict the likelihood of winning a medal based on attributes like age, height, or country.

    • Cluster Analysis: Group athletes by similar characteristics and analyze how these groups perform.

    • Further Data Cleaning: Handle outliers or missing data more comprehensively.

    • In-depth Country Analysis: A more granular analysis of countries' performance across different sports or eras.

Data Visualization

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