This project introduces a versatile machine learning pipeline for classification tasks, employing a modular design using object-oriented programming. Featuring algorithms such as XGBoost, Random Forest, CatBoost, Gradient Boost, and AdaBoost, it facilitates easy comparison of their performances. The key highlights include an OOP design for clear and reusable code, modular components for effortless modification, and detailed reports offering insights into algorithm strengths and weaknesses. Installation is straightforward through repository cloning and dependency installation. Prepare your dataset in pandas dataframe format and execute the pipeline for seamless classification analysis.
