.png){kind=link}
a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Bellabeat Wellness Analysis Project
Description: Analyzed Bellabeat device data to uncover insights into user activity, sleep patterns, and overall wellness, providing actionable recommendations to improve user health and product development. Key Contributions: Retrieved and cleaned large datasets from Bellabeat devices and other relevant sources. Conducted exploratory data analysis (EDA) to understand trends in activity levels, sleep patterns, and other health metrics. Created clear and informative visualizations using ggplot2 and Tableau to present key findings to stakeholders. Developed comprehensive reports and dashboards to communicate insights and recommendations. Insights Gained:
User Activity Patterns: Identified peak activity times and suggested targeted notifications to increase user engagement during low-activity periods. Sleep Patterns: Discovered key factors associated with better sleep quality, leading to personalized sleep improvement tips and feature development. Correlation Between Activity and Sleep: Highlighted the importance of balanced daily routines for improving both activity and sleep metrics. User Demographics and Behavior: Provided recommendations for targeted marketing campaigns and tailored product features based on demographic variations. Impact of External Factors: Suggested integrating weather forecasts to provide personalized activity recommendations. Product Usage Insights: Guided product development by identifying popular and underutilized features. Health and Wellness Trends: Enabled the creation of special programs to keep users engaged during significant periods. Technologies Used:
Languages: R, Python Libraries: pandas, numpy, ggplot2, dplyr, seaborn Tools: Jupyter Notebook, RStudio, Tableau