This repository contains a collection of machine learning implementations and experiments, developed as part of academic and practical projects. It covers a range of approaches including:
- Genetic Programming for optimization and model generation
- Ensemble methods such as bagging, boosting, and stacking
- Naive Bayes classifiers for probabilistic learning and text classification
- Evaluation with standard metrics and comparisons across models
- Experiments using example datasets and reproducible pipelines
The goal is to provide hands-on experience in implementing, testing, and analyzing fundamental machine learning algorithms in different contexts and scenarios.