Instructions to use amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
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README.md
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### 1. Clone and Enter the Repository
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```bash
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git clone [https://github.com/amirsohail100/
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cd
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```
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An optimized machine learning pipeline implementing Ensemble Learning (Voting, Bagging, Boosting) on the classic Iris Dataset to achieve high-accuracy multi-class classification.
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### 1. Clone and Enter the Repository
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```bash
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git clone [https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git](https://github.com/amirsohail100/Iris_datase_-with_Ensemble_Learning.git)
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cd Iris_datase_-with_Ensemble_Learning
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```
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An optimized machine learning pipeline implementing Ensemble Learning (Voting, Bagging, Boosting) on the classic Iris Dataset to achieve high-accuracy multi-class classification.
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