From data to insights: Predicting workout types in Strava with Elasticsearch and data frame analytics
Blog post from Elastic
In the final installment of a blog series on Strava data analysis, the authors explore using Elasticsearch and data frame analytics to predict workout types, such as Ride, VirtualRide, Hike, Run, and Yoga, from fitness data. The process involves using a machine learning classification job to analyze the data with initial variables like distance and elevation gain, which resulted in a model with limited accuracy. By adding elapsed time and velocity as additional variables, the model's performance improved significantly, as evidenced by a more promising scatter plot and ROC curve, highlighting elapsed time as a significant factor in classification. The post encourages readers to explore their Strava data using the Elastic Stack and offers resources for further learning, including a book and a free trial of Elastic Cloud.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.