Download the dataset below to solve this Data Science case study on Fitness Data Analysis.
Fitness data analysis involves looking at information about your physical activities and health to understand how your body is performing. This information might come from things like fitness apps, wearables like smartwatches, or records you keep yourself.
The dataset consists of fitness-related metrics collected through a fitness watch over multiple days. Each record in the dataset includes the following attributes:
- Date: The date when the data was recorded.
- Time: The time at which the data was recorded.
- Step Count: The number of steps taken during the recorded interval.
- Distance: The distance covered in meters during the recorded interval.
- Energy Burned: The amount of energy burned in kilocalories during the recorded interval.
- Flights Climbed: The number of flights of stairs climbed during the recorded interval.
- Walking Double Support Percentage: The percentage of time both feet are in contact with the ground while walking.
- Walking Speed: The walking speed in meters per second during the recorded interval.
Your task is to analyze the fitness watch data and address the following problems:
- Perform exploratory data analysis (EDA) to gain insights into the distribution, trends, and patterns of each fitness metric.
- Create visualizations to depict how different metrics vary over time, across different time intervals, or in relation to one another.
- Analyze how step count, distance, energy burned, and other metrics correlate with each other.
- Identify potential patterns in walking efficiency, energy expenditure, and the relationship between step count and walking speed.
- Segment the data into time intervals (e.g., morning, afternoon, evening) based on the recorded timestamps.
- Investigate variations in fitness metrics (e.g., step count, walking speed) during different time intervals.
