Authors :
Sanjan R; Hemanth Kumar
Volume/Issue :
Volume 9 - 2024, Issue 7 - July
Google Scholar :
https://tinyurl.com/4k7zta7f
Scribd :
https://tinyurl.com/hbh26rjh
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUL1287
Abstract :
Alcohol drinking among college student’s is
common problem that can leads to low academic
performance, health issues and risky behavior. When
student’s in college, they often experience more freedom,
which can result to increased consumption of alcohol.
Therefore, this work explores the prediction of student’s
alcohol drinking habits utilizing Machine Learning
techniques. Data set is utilized in this work contains 25
parameters collected by various college students and
using this dataset is employed for Machine Learning
algorithms, such as Random Forest Classifier, Decision
Tree, and Logistic Regression, are employed. This
experiment aims to classify students into categories of
alcoholic, non-alcoholic and maybe alcoholic based on
various influencing factors. The results obtained are
contrasted with various Machine Learning techniques.
Keywords :
Machine Learning, Random Forest Classifier, Decision Tree, Logistic Regression.
References :
- Dilip Singh Sisodia, Reenu Agrawal and Deepti Sisodia, “A Comparative Performance Of Classification Algorithms in Predicting Alcohol Consumption among Secondary School Student’s”, Springer Nature, 2019
- Rijad Saric, Dejan Jokic, and Edhem Custovic, “Identification of Alcohol Addicts among High School Student’s Using Decision Tree Based Algorithm”, Springer Nature, 2020. Doi: https:10.1007/978-3-030-17971-7_69.
- Shuhaida Ismai, Nik Intan Areena and Nik Azlan, Aida Mustaph, “Prediction of Alcohol Consumption among Portuguese Secondary School Student’s: A Data Mining Approach”, IEEE 2018.
- Advait Singh, Mahendra Kumar Gourisaria, Vinayak Singh, Ashish Sharma, “Alcohol Consumption Rate Prediction using Machine Learning Algorithms”, OITS International Conference on Information Technology (OCIT), 2022. DOI: 10.1109/OCIT56763.2022.00026.
- Tincymol M T and Grace Joseph, “Predicting Alcohol Consumption in Student’s Using Data Mining Tool”, Proceedings of the National Conference on Emerging Computer Applications (NCECA), Vol.3, Issue.1, 2021.
- Ali Ebrahimi, Uffe Kock Will, Thomas Schmidt, Amin Naemi and Anette Sogaard Nielse and Marjan “Predicting the Risk of Alcohol Use Disorder Using Machine Learning: A Systematic Literature Review”, IEEE Access, Vol. 9, November 16, 2021.
- Hind Almayyan and Waheeda Almayyan, “Student Alcohol Consumption Prediction: Data Mining Approach”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 4, 2018.
- Santhiya and Nancy Jasmine Goldena, “Comparative Analysis Of Alcohol Consumption Prediction by Using Machine Learning Algorithms”, International Journal of Creative Research Thoughts (IJCRT) Vol. 10, Issue 12 December 2022.
- Ashish Shrestha, “Predicting Student Alcohol Consumption using Machine Learning”, http://aasys.io
- Navdeep Kaur and Williamjeet Singh, “Alcoholic Behavior Prediction through Comparative Analysis of J48 and Random Tree Classification Algorithms using WEKA”, Indian Journal of Science and Technology, Vol. 9, August 2016. DOI: 10.17485/ijst/2016/v9i32/100716.
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- Md. Ariful Islam Arif, Saiful Islam Sany, Farah Sharmin, Md. Sadekur Rahman and Md. Tarek Habib, “Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 11, No. 5, October 2021.
- Wendy Wagster et al., “Forecasting Alcohol Consumption Trends Among College Student’s Using Artificial Neural Network (ANN)”, Proceedings ASEE Gulf Southwest Annual Conference, 2005.
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Alcohol drinking among college student’s is
common problem that can leads to low academic
performance, health issues and risky behavior. When
student’s in college, they often experience more freedom,
which can result to increased consumption of alcohol.
Therefore, this work explores the prediction of student’s
alcohol drinking habits utilizing Machine Learning
techniques. Data set is utilized in this work contains 25
parameters collected by various college students and
using this dataset is employed for Machine Learning
algorithms, such as Random Forest Classifier, Decision
Tree, and Logistic Regression, are employed. This
experiment aims to classify students into categories of
alcoholic, non-alcoholic and maybe alcoholic based on
various influencing factors. The results obtained are
contrasted with various Machine Learning techniques.
Keywords :
Machine Learning, Random Forest Classifier, Decision Tree, Logistic Regression.