Analyzing Consumer Fitness Behavior Using Bellabeat Data
A Python-based case study exploring how daily steps, calorie expenditure, and sleep duration relate to consumer fitness behavior. This project uses public Fitbit data and replicates a real-world analytics workflow: cleaning, merging datasets, generating visual insights, and producing a polished final report.
📌 Project Overview
Bellabeat is a wellness technology company that designs smart devices for women. This case study analyzes user activity patterns to help understand:
Typical daily movement levels
How steps influence calorie burn
Whether activity levels relate to sleep duration
The goal is to extract meaningful behavioral insights that can inform product positioning and user-engagement strategies.
📂 Included in This Repository
- Final Report (PDF)** Complete case study with all charts and written analysis.
bellabeat_executive_summary.pdf
A one-page summary highlighting key insights and recommendations.
bellabeat_analysis.ipynb
Notebook containing the Python code used for data processing and visualization.
🧠 Research Questions
What does the distribution of daily steps reveal about overall activity levels?
How strongly are daily steps associated with calories burned?
Is there a noticeable relationship between daily steps and sleep duration?
🛠️ Methodology (Python Workflow)
Imported and cleaned datasets: daily activity, daily steps, and sleep logs.
Standardized date formats and merged datasets on Id + Date.
Addressed missing values and ensured dataset consistency.
Generated visualizations using matplotlib and seaborn.
Interpreted results and consolidated insights for Bellabeat’s context.
📊 Key Visualizations
All generated charts are stored in:
/images/
Direct image links:
- Daily Steps Distribution Shows how frequently users hit low, moderate, and high step counts.
- Calories vs Total Daily Steps Reveals a clear positive relationship between movement and energy expenditure.
- Daily Steps vs Minutes Asleep Explores whether more active days lead to longer sleep.(The correlation is weak.)
📝 Insights & Interpretation Activity Patterns
Most users average 6,000–9,000 steps/day, below recommended levels.
A smaller group regularly exceeds 15,000 steps/day, showing high activity.
~15–20% record very low steps (<2,000), suggesting sedentary behavior or inconsistent device use.
Calories & Activity
Strong positive correlation between daily steps and calories burned.
Higher-activity users consistently burn more calories.
Sleep Behavior
No clear linear relationship between steps and sleep duration.
Most users sleep 5.5–7.5 hours, slightly below ideal ranges.
🎯 Recommendations
Encourage step-based challenges to promote daily movement.
Highlight calorie-burn tracking in Bellabeat's marketing materials.
Treat sleep metrics as a complementary feature rather than activity-dependent.
Help users focus on long-term habit trends, not short-term fluctuations.
Make sure the following are installed:
- Python 3.10+
- pip (Python package manager)
- Jupyter Notebook or JupyterLab
pip install pandas matplotlib jupyter
jupyter notebook
bellabeat_analysis.ipynb
Open bellabeat_analysis.ipynb and run cells sequentially.