This project provides an in-depth analysis of marathon data across Canada and the US for the year 2020. The goal is to uncover key insights about marathon performances, focusing on various aspects such as age, gender, race length, and seasonal variations.
The analysis aims to:
Clean and preprocess marathon data to focus on relevant events. Visualize performance trends across different demographics and conditions. Provide actionable insights into marathon performance optimization. Data The dataset used for this analysis is sourced from Kaggle: The Big Dataset of Ultra Marathon Running.
The analysis involves several key steps:
Data Cleaning: Filtering the dataset to include only 50km and 50mi races held in 2020, and removing redundancies. Data Processing: Handling missing values, transforming data types, and extracting relevant features. Data Visualization: Using Seaborn to create visualizations that reveal performance trends.
Key insights from the analysis include:
Performance by Age: Identifying top and bottom performing age groups for 50mi races. Gender Comparison: Comparing average speeds between male and female athletes. Seasonal Performance: Analyzing how performance varies across different seasons, with athletes performing best in spring and worst in summer.
Python: The core programming language used for the analysis. Pandas: For data cleaning and preprocessing. Seaborn: For data visualization. Jupyter Notebook: For interactive analysis and documentation.