In this README, we perform EDA on a dataset of ultra marathon races using Python and libraries like pandas, numpy, seaborn, and matplotlib.
Data Loading: We start by loading the dataset using pandas.
Data Exploration: We explore the dataset's basic information, such as its dimensions and data types. Summary statistics are also generated to understand the data.
Data Analysis: Key analyses include counting event distances, analyzing athlete countries, and checking for UK-based athletes.
Data Cleaning: Data cleaning steps involve selecting specific race distances, focusing on events in the USA during 2022, and performing various data cleaning tasks like handling dates, age, and performance.
Data Visualization: We create various visualizations to answer specific questions, such as:
- Histograms of race lengths.
- Distribution of athlete average speed in 50-mile runs.
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Analysis of average speeds by age groups in different race lengths.
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Analysis of the impact of the season on athlete speeds.
Data Export: Finally, the cleaned and processed data is exported to a CSV file for further analysis or reporting.
Difference in Speed of Males & Females: We visualize and compare the speed of male and female athletes in 50km, 50mi, and 100mi races.
Fastest and Slowest Age Groups in 50-mile Races: We identify the age groups with the highest and lowest average speeds in 50-mile races.
Fastest and Slowest Age Groups in 50km Races: Similar to the previous question, but for 50km races.
Fastest and Slowest Age Groups in 100-mile Races: Similar to the previous question, but for 100-mile races.
Impact of Season on Athlete Speed: We analyze the effect of the season (spring, summer, fall, or winter) on athlete speeds.
The final cleaned and processed dataset is exported to a CSV file named 'USA_Events_2022.csv' for further analysis or reporting.
The data for this dashboard is sourced from https://www.kaggle.com/code/rizzle/exploring-ultra-marathon-dataset