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Sleep Health and Lifestyle

This synthetic dataset contains sleep and cardiovascular metrics as well as lifestyle factors of close to 400 fictive persons.

The workspace is set up with one CSV file, data.csv, with the following columns:

  • Person ID
  • Gender
  • Age
  • Occupation
  • Sleep Duration: Average number of hours of sleep per day
  • Quality of Sleep: A subjective rating on a 1-10 scale
  • Physical Activity Level: Average number of minutes the person engages in physical activity daily
  • Stress Level: A subjective rating on a 1-10 scale
  • BMI Category
  • Blood Pressure: Indicated as systolic pressure over diastolic pressure
  • Heart Rate: In beats per minute
  • Daily Steps
  • Sleep Disorder: One of None, Insomnia or Sleep Apnea

Check out the guiding questions or the scenario described below to get started with this dataset! Feel free to make this workspace yours by adding and removing cells, or editing any of the existing cells.

Source: Kaggle

1 hidden cell

📊 Visualization ideas

  • Boxplot: show the distribution of sleep duration or quality of sleep for each occupation.
  • Show the link between age and sleep duration with a scatterplot. Consider including information on the sleep disorder.

🔍 Scenario: Automatically identify potential sleep disorders

This scenario helps you develop an end-to-end project for your portfolio.

Background: You work for a health insurance company and are tasked to identify whether or not a potential client is likely to have a sleep disorder. The company wants to use this information to determine the premium they want the client to pay.

Objective: Construct a classifier to predict the presence of a sleep disorder based on the other columns in the dataset.

Check out our Linear Classifiers course (Python) or Supervised Learning course (R) for a quick introduction to building classifiers.

You can query the pre-loaded CSV files using SQL directly. Here’s a sample query:

Spinner
DataFrameas
df
variable
SELECT *
FROM 'data.csv'
LIMIT 10
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DataFrameas
df1
variable
-- List the 5 companies that attracted the most funding
SELECT
	company AS "Company",
	funding / 1000000000 AS "Funding / $1B",
	valuation / 1000000000 AS "Valuation / $1B"
FROM companies
	INNER JOIN funding ON companies.company_id = funding.company_id
ORDER BY funding DESC
LIMIT 5
Spinner
DataFrameas
df2
variable
SELECT company, city 
FROM companies
JOIN funding ON companies.company_id = funding.company_id
LIMIT 10

Ready to share your work?

Click "Share" in the upper right corner, copy the link, and share it! You can also easily add this workspace to your DataCamp Portfolio.