🎯1. Business Task

Bellabeat, a health-focused technology company for women, wanted to better understand how consumers use fitness-tracking devices in their daily lives. By analyzing smart device usage data, the goal was to identify trends in physical activity, sedentary behavior, and calorie expenditure that could help inform Bellabeat's marketing strategy, user engagement initiatives, and future product development.

📊2. Dataset

This analysis used the Fitbit Fitness Tracker Dataset provided through the Google Data Analytics Professional Certificate Capstone Case Study. The dataset contains anonymized daily activity data collected from 33 Fitbit users, including metrics such as daily steps, calories burned, distance traveled, and activity intensity levels.

The dataset was cleaned, validated, merged, and analyzed using Excel and Python before being visualized through an interactive Tableau dashboard.

Python was introduced after Excel validation to improve scalability, reproducibility, and analytical capability. While Excel was used to validate dataset integrity, Python was used to load, combine, and prepare the data for deeper behavioral analysis.

🔍3. Key Findings

1. Sedentary behavior dominated daily activity patterns.

Users averaged 992.5 sedentary minutes per day, making sedentary time the largest recorded activity category by a significant margin. This suggests many users spend the majority of their day inactive despite engaging in some physical activity.

2. Users demonstrated moderate daily activity levels.

Participants averaged 7,281 daily steps, 5.2 miles traveled, and 2,266 calories burned per day. While active, users generally fell below the commonly referenced 10,000-step benchmark.

3. Increased movement was associated with increased calorie expenditure.

The Steps vs. Calories analysis revealed a moderately positive relationship suggesting that increasing daily movement contributes to higher overall energy expenditure, although other factors may also influence calories burned.

4. Most activity clustered between 4,000 and 10,000 daily steps.

The Daily Steps Distribution showed that most observations occurred within the moderate activity range, while highly active days exceeding 15,000 steps were relatively uncommon.

💡4. Recommendations

💡 Recommendation 1: Reduce Sedentary Behavior

Because sedentary time represented the largest activity category, Bellabeat should explore features that encourage users to move more frequently throughout the day.

• Potential features include:

• Movement break reminders

• Sedentary behavior detection

• Inactivity notifications

• Daily movement streaks

• Activity-based rewards and challenges

These interventions may help users reduce prolonged inactivity while building healthier daily habits.

💡 Recommendation 2: Encourage Additional Daily Movement

The analysis showed a moderately positive relationship between step counts and calories burned. Bellabeat should leverage this relationship by motivating users to increase daily movement.

• Potential strategies include:

• Personalized step goals

• Step streak tracking

• Progress notifications

• Activity challenges

• Reward systems tied to movement milestones

Encouraging additional movement may help users improve overall activity levels and calorie expenditure.

💡 Recommendation 3: Support Progressive Activity Growth

Most users demonstrated moderate activity levels and fell below the commonly referenced 10,000-step benchmark. Rather than focusing on aggressive fitness goals, Bellabeat should encourage gradual improvement through achievable milestones.

• Potential features include:

• Incremental goal progression

• Personalized coaching insights

• Daily activity reminders

• Progressive movement challenges

• Goal achievement celebrations

These interventions may help users transition from moderate activity levels toward more active lifestyles.

📓5. Executive Summary

This project analyzed Fitbit fitness tracker data to identify user activity trends and provide business recommendations for Bellabeat. Using Excel, Python, and Tableau, I cleaned, validated, merged, and analyzed activity data from 33 users. The analysis revealed high sedentary behavior, moderate activity levels, and a moderately positive relationship between daily steps and calories burned. Findings were translated into actionable recommendations focused on increasing user engagement and promoting healthier activity habits.

⚙️Project Deliverables

• Data Validation

• Data Cleaning

• Dataset Integration

• Exploratory Data Analysis

• Executive Dashboard

• Business Recommendations

🔨Tools Used

• Excel

• Python

• Pandas

• Numpy

• JupyterLab

• Tableau

Interactive Tableau Dashboard