Data analysis on ultra-marathon running races

The aim of this project is to conduct a comprehensive data analysis and exploratory data analysis (EDA) on a dataset related to ultra-marathon running using python.

Objectives:

Understand the Dataset:

Gain familiarity with the dataset, including its structure, variables, and overall content.

Identify key attributes and metrics related to ultra-marathon performance.

Data Cleaning and Preprocessing:

Handle missing values, outliers, and inconsistencies within the dataset.

Perform data transformation and normalization where necessary to prepare for analysis.

Exploratory Data Analysis (EDA):

Utilize descriptive statistics to summarize the main characteristics of the data.

Generate visualizations to identify patterns, trends, and correlations within the dataset.

Examine the distribution of key variables such as race times, distances, age of participants, and environmental conditions.

Identify Key Insights:

Analyze performance metrics and factors that influence ultra-marathon outcomes.

Investigate correlations between variables such as age, gender, training practices, and race performance.

Explore environmental and course-related factors that impact race results.

Questions to answer :

Difference in speed for the 50km,50 mi male to female ?

What age groups are the best in the 50 mi race ( age of athletes 20+ )?

Seasons for data --> slower in summer or winter?

Males athletes are faster or females ?