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 1 | # EDA marathon running using python
 2 | This repository contains an exploratory data analysis (EDA) of a comprehensive dataset on ultra-marathon running events. The dataset spans over two centuries and provides detailed information on various ultra-marathon races, athletes, and their performances.
 3 | 
 4 | ## Dataset
 5 | The dataset used in this analysis is sourced from Kaggle: [The Big Dataset of Ultra-Marathon Running](https://www.kaggle.com/datasets/aiaiaidavid/the-big-dataset-of-ultra-marathon-running). It includes information on race events, distances, athlete demographics, and performance metrics.
 6 | 
 7 | ## Analysis Overview
 8 | The Jupyter notebook `EDA_marathon_running.ipynb` covers the following steps:
 9 | 
10 | 1. **Data Acquisition**
11 |     - Downloading the dataset using the Kaggle API.
12 |     - Extracting the data from the downloaded zip file.
13 | 
14 | 2. **Data Preprocessing**
15 |     - Loading the dataset into a pandas DataFrame.
16 |     - Initial exploration of the dataset (shape, data types, missing values).
17 |     - Filtering the dataset for specific criteria (e.g., events in 2020, races held in the USA).
18 |     - Cleaning and transforming data columns for analysis.
19 | 
20 | 3. **Exploratory Data Analysis (EDA)**
21 |     - Visualizing the distribution of race distances and athlete genders.
22 |     - Analyzing the relationship between athlete age and performance.
23 |     - Comparing average speeds across different event distances and genders.
24 | 
25 | ## Visualizations
26 | Several visualizations are generated to understand the data better:
27 | - Count plots for event distances and athlete genders.
28 | - Violin plots for athlete speeds across event distances.
29 | - Line plots showing the relationship between athlete age and average speed.
30 | 
31 | ## Dependencies
32 | To run the notebook, you need the following Python libraries:
33 | - pandas
34 | - seaborn
35 | - matplotlib
36 | - kaggle
37 | 
38 | ## Conclusion
39 | This analysis provides insights into ultra-marathon running events, highlighting trends and patterns in athlete performances based on gender, age, and event distances. Further analysis can be conducted to explore additional aspects of the dataset.
40 | 
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42 | 


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