BellaBeat Fitness
A Case Study

Key Questions
1. What are some trends in smart device usage?
2. How could these trends apply to Bellabeat customers?
3. How could these trends help influence Bellabeat marketing strategy?.

Data Preparation and Processing

The data set to be used is a public data set available through Kaggle and contains personal fitness tracker from thirty fitbit users. The data set includes 18 csv files that capture everything from daily activity, calories (daily, hourly and by minute), intensities (daily, hourly and by minute), number of steps (daily, hourly and by minute), heart rate, minute METs, sleep (Day and minute) and weight log info. For the scope of this data analysis only a selection of the 18 data sets that were deemed relevant in addressing the business task were imported into RStudio.

BELLABEAT BIG QUERY INPUTS
Using the Google BigQuery platform, I selected the required data for analysis and this can be seen using the link below.

• SQL analysis

Data vizualization on the three components

1. Activity level and calories burnt relation From the above chart we can see that a person who has higher active minutes tends to burn more calories in a day, the more time they spend inactive, the lower calories they tend to burn in a day.

2. Activity level and sleep quality relation From the above chart we can see that a person who has higher active minutes tends to burn more calories in a day, the more time they spend inactive, the lower calories they tend to burn in a day.

Recommendation and act
Conclusion
From the analysis result, it is clear that there is a clear trend in non-active people having a negative lifestyle. The three relations we found during the analysis includes:
1. Very-active minutes has a positive relation to calories burnt
2. Active person has a positive relation to sleep quality
3. Non-active person is more likely to have a high BMI
Recommendations to business
As these relations are the analysis results of participants who use smart devices to track their activity statistics, we can apply these to make data-driven decisions on Bellabeat future products/functionality:
Bellabeat can include function in Bellabeat app to alert user who tends to have a high number to sedentary minutes
Bellabeat can include timely notification in Leaf/Time to motivate user to move around regularly to reduce their sedentary minutes
Bellabeat can use the relation between high sedentary minutes and BMI to promote an active lifestyle can reduce body fat and create better health with Bellabeat products
Bellabeat can further enhance their sleep tracking function to promote the sleep/non-active relation. Use this as an incentive to purchase Bellabeat products: create a better sleeping habit by being more active in everyday life.