This repository focuses on building and evaluating a high-performance machine learning pipeline on the classic Iris Dataset using advanced Ensemble Learning methodologies. The goal is to optimize multi-class classification accuracy by combining multiple base estimators.
To achieve robust predictive stability, the project utilizes the following ensemble architectures:
The system processes the standard Iris dataset containing 150 instances tracking four core physical features:
Follow these quick implementation steps to clone, configure, and execute the ensemble model pipeline locally on your machine:
1git clone [https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git](https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git)
2cd Iris_datase_-with_Ensemble_Learning
3pip install -r requirements.txt
An optimized machine learning pipeline implementing Ensemble Learning (Voting, Bagging, Boosting) on the classic Iris Dataset to achieve high-accuracy multi-class classification.
