Bellabeat Case Study
Table of Contents
About The Project
The main focus of this case is to analyze smart devices fitness data and determine how it could help unlock new growth opportunities for Bellabeat.
Bellabeat is a high-tech company that manufactures health-focused smart products.They offer different smart devices that collect data on activity, sleep, stress, and reproductive health to empower women with knowledge about their own health and habits.
This data can help users better understand their current habits and make healthy decisions. The Bellabeat app connects to their line of smart wellness products
Built With
This section should list any major frameworks/libraries used to built your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
Getting Started
ASK
Identify trends in how consumers use non-Bellabeat smart devices to apply insights into Bellabeat’s marketing strategy.
Dataset
The data source used for our case study is FitBit Fitness Tracker Data. This dataset is stored in Kaggle and was made available through Mobius.
Information About The Dataset
These datasets were generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. Variation between output represents use of different types of Fitbit trackers and individual tracking behaviors / preferences.
Data Organization and verification
Available to us are 18 CSV documents. Each document represents different quantitative data tracked by Fitbit. The data is considered long since each row is one time point per subject, so each subject will have data in multiple rows.Every user has a unique ID and different rows since data is tracked by day and time.
Process
Knowing the datasets, I have uploaded the datasets that will help us answer our business task. I cleaned and combined hourly data in colab as big query charges money to export files. To combine daily data I used SQL workbench. To follow steps, I have provied both the notebook and and sql script.
Analyze and Share
Conclusion
Recommendations:
-
We can encourage customers to reach at least daily recommended steps by CDC - 8.000 sending them alarms if they haven't reached the steps and creating also posts on our app explaining the benefits of reaching that goal.
-
Based on our results we can see that users sleep less than 8 hours a day. They could set up a desired time to go to sleep and receive a notification minutes before to prepare to sleep.
-
We are aware that some people don't get motivated by notifications so we could create a kind of game on our app for a limited period of time. Game would consist in reaching different levels based on amount of steps walked every day.