A Machine Learning Driven Analysis to Determine the Correlation Between Parenting Style and Offspring’s Behavior

Parents are usually the most important person for a human being as they encourage and support an offspring’s physical, emotional, social, and intellectual development from infancy to maturity. Individual child faces various challenges as they grow up. Proper parenting plays a prominent role in handling and abating those challenges. This paper aims to show various consequences of the attachment style and handling of depression, anxiety, stress, and anger due to different types of parenting styles. These consequences of parenting styles are to be figured out in an automated way so that one can acknowledge these factors on their own and bring various positive changes to their parenting. This research makes use of machine learning algorithms in order to differentiate between various parenting styles through various aspects of their life such as stress, anxiety, depression, attachment style, anger management, etc. We collected the research data from students (school, college, university) through a survey that was developed in collaboration with psychologists and counselors, which consisted of 2206 survey responses. Afterward, the survey data was stored and pre-processed. Then, machine learning algorithms such as Support Vector Machine, KNN, Random Forest, XG-BOOST and Decision Tree are utilized to detect parenting style by analyzing the effects of parenting on their offspring and the accuracy of these models are 84.65%, 76.98%, 87.30%, 87.30% and 85.185% respectively.