Technology Used: Python, NumPy, Pandas, MatplotLib, Seaborn
This case study is a part of the Google Data Analytics Certificate.
The task is to analyze smart device data and present findings along with high-level recommendations for Bellabeat's marketing strategy by assuming the role of a junior data analyst working on the marketing analyst team at Bellabeat.
Source: Bellabeat-Case-Study-Task-Description
- Prepare and process a dataset on smart fitness device
- Analyze smart device fitness data usage in order to gain insight into how consumers use non-Bellabeat smart devices
- Apply these insights on Bellabeat Products and provide recommendaions for Bellabeat's marketing strategy
Bellabeat is a high-tech company that manufactures health-focused smart products for women. Their mission is to empower women to reconnect with themselves, unleash their inner strengths and be what they were meant to be.
They have a range of products such as Leaf (wellness tracker), Time (wellness watch), Spring (smart water bottle), Bellabeat app & membership, etc that collect data on activity, sleep, stress, and reproductive health and provide women with knowledge about their own health and habits.
Documentation: Dataset
The dataset being used is FitBit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius). This Kaggle data set contains personal fitness tracker from thirty fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits
Documentation: Data Processing
The data is divided into 3 types: Daily, Hourly and by Minute
The data processing is performed in python file: Data Cleaning.ipynb
Documentation: Data Analysis
Data Analysis and Visualization is performed in python file: Data Analysis.ipynb
Documentation: Summary
The analysis of personal fitness data from fibit users yeilded key insights on:
- how active users are, how intense are these activities and during what time of day they are performed
- how many hours of sleep do users get and on what days they get the most sleep
- how often do users use their fitbit and for how long
Based on these insights, recommendations were provided on marketing strategies for Bellabeat products