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The dataset includes 374 individuals and 13 variables covering sleep duration, sleep quality, exercise frequency, stress levels, diet, occupation type, and sleep disorders. I used pandas, visualization tools, and exploratory analysis to identify patterns and potential predictors of healthier sleep.

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Ranking-Occupations-Tiredness-Level-and-Sleep-Using-Python

I just wrapped up a Python project analyzing anonymized sleep data from SleepInc’s new app, SleepScope, and it was one of the most interesting datasets I’ve worked with yet 😴📊 Using six months of lifestyle averages for 374 users, I explored how exercise habits, gender, occupation, stress levels, and sleep disorders relate to sleep quality. This project was a great mix of:

✨ Data wrangling with pandas

✨ Exploratory visualizations

✨ Identifying key patterns in what helps or harms sleep

✨ Turning real-world behavioral data into actionable insights

It’s always fun digging into datasets that actually connect to daily life — and sleep is one of the most important (and underrated!) pillars of health.

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The dataset includes 374 individuals and 13 variables covering sleep duration, sleep quality, exercise frequency, stress levels, diet, occupation type, and sleep disorders. I used pandas, visualization tools, and exploratory analysis to identify patterns and potential predictors of healthier sleep.

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