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jatinmonga-ai / repository
End-to-end fitness lifestyle data analysis using Python, Pandas, and Jupyter Notebook with visual insights.
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This project analyzes a synthetic lifestyle and fitness data collected over 31 days to identify trends, correlations, and insights about daily activity, sleep, hydration, and mood. The analysis uses Python and popular data science libraries to perform exploratory data analysis (EDA) and visualize relationships between different metrics.
The dataset lifestyle_fitness_data.csv contains the following columns:
| Column Name | Description |
|---|---|
| Date | Date of the record |
| Steps | Number of steps taken daily |
| Calories | Calories burned per day |
| ActiveMinutes | Minutes of physical activity |
| SleepHours | Number of hours slept |
| WaterIntake | Daily water intake in liters |
| MoodScore | Daily mood rating on a scale from 1 to 10 |
Note: The dataset is synthetically generated using Python to simulate real-world fitness and lifestyle data.
The following visual analyses are included in this project:
fitness_lifestyle_data.ipynb in Jupyter Notebook.