https://github.com/kianaasd93/fitness-data-analysis-

Fitness Data Analysis and Mathematical Modeling Decision Support at Trivisio
https://github.com/kianaasd93/fitness-data-analysis-

dataanalysis-projects fitness fitness-tracker jupyter jupyter-notebook python

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Fitness Data Analysis and Mathematical Modeling Decision Support at Trivisio

• Host: GitHub
• URL: https://github.com/kianaasd93/fitness-data-analysis-
• Owner: kianaasd93
• Created: 2024-06-19T11:49:14.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-19T11:52:09.000Z (5 months ago)
• Last Synced: 2024-10-12T14:42:36.838Z (about 1 month ago)
• Topics: dataanalysis-projects, fitness, fitness-tracker, jupyter, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 5.69 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fitness-Data-Analysis-

Fitness Data Analysis and Mathematical Modeling Decision Support at Trivisio

Trivisio is a company that develops cutting-edge wearable devices, with a particular focus on promoting the Colibri Wireless. The Colibri Wireless unit consists of three sensors dedicated to measuring various parameters. The sensor positions include 1 IMU over the wrist on the dominant arm, 1 IMU on the chest, and 1 IMU on the dominant side's ankle. These sensors provide information on different features, including timestamp (seconds), activity ID, heart rate (bpm), temperature, 3D-acceleration scale (±16g, resolution: 13-bit), 3D-acceleration scale (±6g, resolution: 13-bit), 3D-gyroscope, 3D-magnetometer, and orientation. The company's primary objective is to position its product as superior to key competitors in the market.

To gain a competitive edge, Trivisio has collected comprehensive data from nine individuals, both male and female, aged 24-31, who wore three IMUs and a heart-rate monitor. Each subject participated in 12 recommended physical activities, such as walking, cycling, and playing football, with some engaging in an additional six optional activities. The data was collected to evaluate the performance of the Colibri Wireless unit during various physical activities and to gather information on the subjects' movements. Additionally, information about each candidate, such as Subject_ID, Sex, Age_(years), Height_(cm), Weight_(kg), Resting HR_(bpm), Max_HR_(bpm), and Dominant_hand, is available.

Prior to analysis, the dataset underwent a meticulous preprocessing phase where issues such as missing values, outliers, and inconsistencies were addressed. This preprocessing ensures that our analysis is based on the most accurate and representative data possible.

The primary objectives of this analysis is To Uncover Patterns and Trends by examining the dataset, we aim to identify underlying patterns and trends that could inform our strategic decisions. To Develop Predictive Models, Utilizing advanced mathematical modeling techniques, we intend to develop models that can predict future trends or outcomes based on the data. To Show important features.To Provide Actionable Insights, The ultimate goal is to translate these data-driven insights into actionable recommendations that can drive value for Trivisio. This report is structured to take you through this journey of data exploration, analysis, and model development, culminating in strategic recommendations based on our findings.