FitBitCaseStudy

FitBit Case Study: Understanding Mood and its Impact on Human Behavior and Lifestyle

This case study is based on data retrieved from FitBit bands, aiming to predict how mood influences human behavior and identify which mood contributes to a healthier lifestyle. The data analysis follows a structured approach:

Approach:

1. Data Extraction:

• The initial step involves extracting data from FitBit bands, and capturing information related to physical activity, sleep patterns, and mood.

2. Data Cleaning:

• To ensure accuracy and reliability, a thorough data cleaning process is implemented. This includes handling missing values, addressing outliers, and formatting the data for further analysis.

3. Visualization:

• The primary focus is on visualizing the data based on self-assessed questions, with an option for discussion on additional relevant questions.

Self-Assessed Questions:

1. In which mood do people walk more?

• Exploration of the correlation between mood and walking activity to identify the mood associated with increased physical activity.

2. In which mood do people sleep more?

• Analysis of sleep patterns about mood, aiming to determine the mood linked to longer and more restful sleep.

3. In which mood do people burn more calories?

• Examination of the relationship between mood and calorie burn, shedding light on the mood that contributes to higher energy expenditure.

4. In which day of the week are people more active?

• Utilizing data to identify the days of the week when individuals tend to be more physically active.

5. In which day of the week are people more lazy?

• Analyzing data to pinpoint the days when individuals exhibit lower levels of physical activity, indicating a more relaxed or sedentary state.

Discussion:

• Open to adding any additional self-assessed questions or topics for discussion that may enhance the insights gained from the FitBit data.

This readme provides an overview of the case study, outlining the methodology and key questions addressed during the data analysis. Further details on the findings and insights can be explored within the project documentation.

THE MAIN INSIGHT THAT I PREDICTED FROM THE DATA IS AS BELOW

Individuals who experience happiness tend to engage in increased physical activity, including walking more frequently. This heightened level of activity not only leads to the burning of additional calories but is also indicative of a positive correlation between emotional well-being and a commitment to a healthy lifestyle. Moreover, the positive influence of happiness extends to one's sleep patterns, with content individuals demonstrating a proclivity for well-balanced and restorative sleep. In contrast, the data suggests that those in a neutral mood may exhibit a tendency towards lethargy and reduced physical activity.