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A Deep Dive Into The Causes and Results of Poor Sleep

Context

As a health insurance company, we have noticed a rise in sleep disorders leading to increased expenditure. We need to causes and trends to try reduce this expenditure.

Expected Outcomes

  • Identify trends in sleep disorders
  • Identify causes of sleep disorders
  • Create a dashboard summarising findings
  • Present findings to stakeholders

Tools Used

  • PowerBI
  • DAX
  • Python

Output

See file named "Sleep EDA".

Conclusions

  • High BMI is correlated with sleep disorders
  • More steps were correlated with a lower weight
  • Sleep disorders are more common later in life and in women
  • Occupation correlated with the amount of sleep and the likelihood of a sleep disorder

Recommendations

  • Create incentives to lose weight. Incentives that target increasing daily step count are likely more effective than those that promote increased physical activity
  • Consider revisiting prices for women and those who are older
  • Create awareness campaigns targeted at people with occupations known to decrease the amount of sleep and increase the likelihood of a sleep disorder, so that they are more likely to take mitigating steps such as limiting stimulants in the afternoon

Acknowledgements

I want to thank my team members for their contributions:

  • Tevin Dikgale (Team lead)
  • Abdulmalik Adeyemo
  • Olanike Oluwaseyi
  • Emmanuel Osayande

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