screen / sleep time

(1%) Describes and motivates a real-world problem where data science may provide helpful insights. Your description should be easily understood by a casual reader and include citations to motivating sources or relevant information (e.g. news articles, further reading links … Wikipedia makes for a poor reference but the links it cites are usually promising).

The relationship between screen time and sleep is a real-world problem where data science may provide helpful insights. Many people spend a significant amount of time on devices with screens such as smartphones, tablets, and computers. This has raised many concerns about the impact of screen time on sleep quality/duration. Data science can help to shed light on this issue by analyzing large datasets of screen time and sleep patterns collected from large populations over large periods of time. By using statistical models and machine learning algorithms, data scientists can identify patterns and correlations that may be difficult to detect using traditional methods. This type of data-driven approach can provide valuable insights into the relationship between screen time and sleep, and influence interventions and policies aimed at promoting healthy sleep habits in today's digital age.

The most common belief surrounding the relationship between screens and sleep is that time on screens decreases time spent asleep. Many attribute this especially to the blue light emmitted from screens. Data science can help researchers confirm the validity of such widespread beliefs.

sources:

https://www.sleepfoundation.org/how-sleep-works/how-electronics-affect-sleep

https://raisingchildren.net.au/preschoolers/play-learning/screen-time-healthy-screen-use/screen-time-sleep

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5839336/

(1%) Explicitly load and show your dataset. Provide a data dictionary which explains the meaning of each feature present. Demonstrate that this data is sufficient to make progress on your real-world problem described above.

import pandas as pd 

sleepCSV = 'Sleep_Analysis.csv'

sleepDF = pd.read_csv(sleepCSV)
sleepDF.head()

	Age	Gender	meals/day	physical illness	screen time	bluelight filter	sleep direction	exercise	smoke/drink	beverage	sleep time
0	22	Male	two	no	2hrs	yes	west	sometimes	no	Tea	6.7575
1	22	Female	three	no	3-4 hrs	no	south	no	no	Coffee	8.0000
2	23	Male	three	no	3-4 hrs	no	south	no	no	Tea	8.0000
3	23	Female	two	no	1-2 hrs	no	east	sometimes	no	Coffee	6.5000
4	22	Male	three	no	more than 5	yes	east	sometimes	yes	Tea and Coffee both	6.0000

Data Dictionary

Age: age in years of subject

Gender: sex of subject

Meals/day: number of meals consumer per day by subject

Physical illness: binary yes/no category of whether subject is physically ill

Bluelight filter: binary yes/no category of whether subject uses blue light filter

Sleep direction: caridnal direction that patient faces when sleeping

Exercise: binary yes/no category of whether subject exercises regularly

Smoke/drink: binary yes/no category of whether subject drinks/smokes

Beverage: preferred beverage of patient

Sleep time: average time spent sleeping eaach night

(1%) Write one or two sentences about how the data will be used to solve the problem. Earlier in the semester, we won’t have studied the Machine Learning methods just yet but you should have a general idea of what the ML will set out to do.

Data can be used to help researchers examine for correlations between certain data values. Since this dataset contains information on the duration that subjects spend on screens, the amount of time they spend asleep, whether the subject filters out bluelight, as well as other metrics about the sujects' lives, the data can be used to answer the question of how screen time affects sleep time. We can cluster the data based on sleep duration, blue light filter usage, and screen use.