Health Fitness Tracker System Using Machine Learning Based on Data Analytics

In order to understand consumer behavior and inform marketing tactics for Bellabeat, a high-tech manufacturer of health-oriented products for women, the article analyzes data on smart device usage. The study makes use of Fitbit Fitness Tracker data that was gathered over the course of a month in 2016, with a particular emphasis on variables relating to exercise, sleep, and heart rate. In the study's background, it is said that Bellabeat hopes to increase its market share in the global smart device industry by analyzing data from smart devices. The study emphasizes how crucial it is to comprehend consumer behavior and usage patterns in order to properly direct marketing initiatives. Data preparation and cleaning using RStudio, exploratory data analysis, statistical summaries, and data visualization approaches are among the strategies used in the analysis. According to the report, users’ daily activity levels are trending upward, with variable levels of intensity throughout the day. It shows the average distance users travel at various intensities of activity, showing that users go farther at low intensities than at higher ones. The data also shows a link between daily steps taken and calories expended, lending credence to the idea that higher levels of physical activity result in higher calorie expenditure.