• Mr. Yash Haryan
• Mr. Shreyas Parab
• Mr. Harsh Bari
• Mr. Harsh Tiwari
• Mr. Gaurav Shete

Alcohol and drug addiction pose significant public health challenges, leading to numerous social, psychological, and health-related issues. The detection of addiction patterns early on can aid in timely intervention, improving patient outcomes. In this project, we propose a Machine Learning (ML)-based solution for detecting alcohol and drug addiction using publicly available datasets. Our model leverages multiple algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks to analyze behavioral patterns, social media activity, and physiological markers from the datasets. We focus on creating an accurate prediction system that not only identifies addiction but also classifies the severity of the addiction to offer personalized interventions. Kaggle datasets related to substance use and mental health behaviors are used to train and validate the model, ensuring its robustness and generalizability across different populations. Key outcomes of this project include the identification of high-risk individuals based on specific behavioral cues and personalized addiction recovery recommendations using AI-powered insights. This system has the potential to revolutionize addiction recovery by providing a scalable, data-driven solution.

- Machine Learning, Alcohol Addiction, Drug Abuse Detection, Social Media Analysis, Predictive Models, Neural Networks.

"Detection of Alcohol and Drugs Addiction using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.k370-k374, April-2025, Available :http://www.jetir.org/papers/JETIR2504A45.pdf

"Detection of Alcohol and Drugs Addiction using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppk370-k374, April-2025, Available at : http://www.jetir.org/papers/JETIR2504A45.pdf