Screen Time Addiction and Psychological Metric Analysis among Teens Using Machine Learning Techniques

Mobile Screen spent by teenage who use lot of time on screen
experience psychiatric problems such as frustration, fatigue, depression,
anxiety, and so on. Matplotlib pyplot with histogram figure is used to evaluate
the psychiatric condition data (CSV file). For the purpose of rating the
condition data, the standard deviation and mean are determined. For data
analysis, there is a correlation matrix that compares every row and column.
Gradient Booster, Logistic Regression, Knn algorithm is used to compare the
precision of the machine learning process, which merged the characteristics of
addictive and non-addictive data to find rating disorders.
Keyword—data science, gradient booster, logistic regression, pyplot
1 Introduction
Electronic device dysfunction is a symptom of neurological addiction induced by
the use of devices such as the internet, sports, and social media. Anxiety and
depression leads to health problem in the whole world and it affects all group all men,
women and kids [5]. All the outcome of anxiety and depression disorder results in
weight loss or gain and psychological disorder like tension, fear (Much more fear
avoid the situation that involve certain negative judgment), anger, low [6]
concentration and many. A questionnaire study of students [1, 2] forecasts the
addictive diseases fatigue, depression, and anxiety [3] as a result of long-term screen
use and addiction. The screen addictive disease details was interrelated from a book
called Diagnostic and predictive manual of psychiatric disorders, which has been a
worldwide used manual since its third edition [4]. A Python system that incorporates
variation of many algorithms comes under machine learning, which are of two type
called as supervised and unsupervised in machine learning. The coding,
representation, symbols, characters, script, signs and figures are all available for free
from http://scikit-learn,sourceforge.net, which has performance, documentation and
API consistency [3].144http://www.i-jim.org