This repository contains code and resources for building predictive models to determine the likelihood of an individual using alcohol and nicotine based on their demographic and personality traits.
The goal of this project is to develop machine learning models that can predict the probability of alcohol and nicotine usage by individuals. The predictive models are trained using demographic data (such as age, gender, education level) and personality traits (e.g., openness, conscientiousness) as features.
- Data preprocessing and exploration
- Model development and evaluation
- Hyperparameter tuning
- Model comparison and selection
- Python
- Scikit-learn
- Pandas
- Matplotlib
- Seaborn
The dataset used in this project is sourced from [https://www.kaggle.com/datasets/mexwell/drug-consumption-classification].