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SUBTIQ AI – predictive analytics platform for behavioral risk detection
An AI-powered web application designed to identify behavioral risk patterns and support the early detection of substance-use tendencies using machine learning and data analysis.
Overview
As part of a multidisciplinary team, I contributed to the development of a predictive analytics system that analyzes behavioral and digital usage patterns to estimate potential risk indicators. The project involved building and evaluating multiple machine learning models and creating a functional web-based prediction system.
My contributions
*Data preprocessing and feature preparation
*Machine learning model evaluation
*Prediction pipeline implementation
*UI and web application development
*System testing and deployment support
Technologies used
Python, Pandas, Scikit-learn, CatBoost, Random Forest, Logistic Regression, Flask, SQL
Key outcomes
Developed a working end-to-end ML prediction pipeline
Evaluated multiple models, with CatBoost achieving approximately 99% accuracy
Built a functional web application for risk prediction
Created a structured workflow for data preprocessing, prediction, and performance evaluation
This project was presented at the 14th International Conference on Contemporary Engineering and Technology (ICCET 2026) and reflects my interest in applying AI and data-driven systems to real-world problem solving.
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