📌 Business Task
Analyze smart device usage data to identify user behavior trends. Use these insights to inform the marketing strategy for the Bellabeat Leaf product.
📂 Data Source
Fitbit Fitness Tracker dataset available on Kaggle. The dataset includes daily activity, sleep, and calorie data from 30 users over 31 days.
🛠 Tools Used
- R (tidyverse, lubridate, ggplot2, janitor)
- Google Sheets
- Tableau (for optional visualization)
🔍 Process
1. Ask
What trends in smart device usage can Bellabeat use to better market their products?
2. Prepare
Downloaded and reviewed the structure of the Fitbit CSV files. Focused on dailyActivity_merged and sleepDay_merged files.
3. Process
- Cleaned column names
- Formatted date columns
- Joined activity and sleep data by user ID and date
4. Analyze
- Average daily steps: < 7,000 for most users
- Positive correlation between active minutes and calories burned
- Users who tracked sleep averaged ~6.8 hours
5. Share
Created charts in ggplot2 to visualize step count, sleep distribution, and calorie patterns.
6. Act
- Promote daily step goals through app reminders
- Encourage consistent sleep tracking
- Target women with irregular usage habits using personalized engagement
🔗 GitHub Repository
View the full R code and notebook on GitHub (add link here)