Date:
Team Members:
- Modris
- Shima
- Sheryll
Project Description:
A brief overview goes here explaining the purpose of our project and the problem it aims to solve.
Branches Created:
- List of Git branches or structure if this is relevant
A short explanation of the industry context or business relevance.
Clearly listed challenges or user pain points being addressed.
Concise hypotheses tied to our problem statements. Followed by the research questions we aim to answer with data.
- Description of primary and secondary datasets used
Column Description Significance Id Unique identifier for each user. Primary Key for user tracking. SleepDay Date and time of recorded sleep data. Useful for analyzing sleep patterns. TotalSleepRecords Number of recorded sleep sessions. Indicates sleep pattern complexity (e.g., interrupted sleep). TotalMinutesAsleep Total minutes spent sleeping in a given day. Key metric for evaluating sleep quality. TotalTimeInBed Total minutes spent in bed (includes awake periods). Helps calculate sleep efficiency. ActivityDate Date of recorded activity data. Useful for time-series trend analysis. TotalSteps Total number of steps taken in a day. Key metric for physical activity. TotalDistance Total distance covered (in miles or km). Reflects total movement intensity. TrackerDistance Distance tracked specifically by the Fitbit device. Ensures device-specific tracking accuracy. LoggedActivitiesDistance Manually logged distance that may differ from tracker data. Useful for identifying manual overrides. VeryActiveDistance Distance covered during intense physical activity. Reflects vigorous workout behavior. ModeratelyActiveDistance Distance covered during moderate physical activity. Reflects medium-intensity workouts. LightActiveDistance Distance covered during light physical activity. Reflects casual movement (e.g., walking). SedentaryActiveDistance Distance covered while mostly inactive. Potentially redundant but may indicate movement while seated. VeryActiveMinutes Total minutes spent doing intense physical activities. Key fitness metric for cardio workouts. FairlyActiveMinutes Minutes spent engaging in moderately intense activities. Helps in understanding balanced activity levels. LightlyActiveMinutes Minutes spent doing light physical activities. Important for understanding general mobility. SedentaryMinutes Total sedentary minutes recorded. Critical for identifying sedentary lifestyle risks. Calories Total calories burned in a day. Core indicator for energy expenditure and weight management. - Data sources (CSV, API, etc.)
- Key variables or features
- Why this data was chosen
- Summary of the schema
- Entity Relationship Diagram (include image or link)
- Description of tables, relationships, keys
Entities
User (Id, Age, Gender, Occupation, BMI Group)
Sleep (Id, SleepDay, TotalSleepRecords, TotalMinutesAsleep, TotalTimeInBed)
Activity (Id, ActivityDate, TotalSteps, TotalDistance, TrackerDistance, LoggedActivitiesDistance)
Intensity (Id, ActivityDate, VeryActiveMinutes, FairlyActiveMinutes, LightlyActiveMinutes, SedentaryMinutes)
Calories (Id, ActivityDate, Calories)
Relationships
User (PK: Id) → Connected to all other tables via Id.
Sleep and Activity are related using ActivityDate and SleepDay.
The BMI data from secondary dataset can be linked using user attributes such as Age or BMI category.
- Loaded and reviewed dataset structure
- Removed duplicates and handled missing values
- Standardised formats and converted categorical values
add more if needed
- Describe techniques used for exploring data patterns
- Initial observations
- List of queries (e.g. aggregations, JOINs, subqueries)
- Insights
- Description of our visualisation tools used
- Key graphs and insights
- Highlight most important discoveries
- Summary of what our analysis means for the business
Explain findings that helped improve our process, or decision-making. Pls link back to the original problem statements.
- Tools and technologies used (SQL, Python, Pandas, Seaborn, etc.)
- Describe issues we faced during development and what we learned
sql-database/
│
├── data/ # Raw datasets
├── notebooks/ # Jupyter notebooks for EDA and reporting
├── scripts/ # Python scripts for cleaning and loading
├── schema.sql # SQL database schema
├── queries.sql # All SQL queries used
└── README.md
Presentation: https://docs.google.com/presentation/d/1T8r3NJ6lvzrLjCZWKZQkFa2BNVDzjP75qQgQqAJKmIo/edit#slide=id.p
Trello: https://trello.com/b/TJmjTcdU/ravenclaw-club
Google Doc: https://docs.google.com/document/d/1ee1Irj8KmspckHQ1KdCo3uqa0DlZIPq9wYm0BbSRYAc/edit?tab=t.0