Fitness EDA using Advanced PySpark

Exploratory Data Analysis (EDA) plays a pivotal role in comprehending intricate datasets and deriving meaningful insights. This research focuses on harnessing advanced PySpark and SQL techniques for conducting EDA on FitRec (Fitness Record) data, an extensive dataset obtained from the well-known fitness tracking application, Endomondo. Leveraging PySpark’s distributed computing capabilities and Spark SQL’s querying prowess, this study delves into data preprocessing, cleaning, and visualization. Also, this study enables a comprehensive understanding of users’ fitness patterns and performance. Through interactive analysis and real-world case studies, the study showcases the effectiveness of employing PySpark and SQL for in-depth EDA on FitRec data, delivering valuable knowledge to researchers and data analysts in the fitness domain.