A structured, hands-on collection of notebooks and resources covering ensemble learning methods in machine learning — from foundational bagging techniques to modern gradient boosting frameworks, with a final project applying and comparing these algorithms on a real dataset.
| # | Topic | Description |
|---|---|---|
| 1 | Introduction to Ensemble Learning | Core concepts behind combining multiple models to improve predictive performance |
| 2 | Bagging and Random Forests | Bootstrap aggregation, variance reduction, and the Random Forest algorithm |
| 3 | Boosting and Gradient Boosting | Sequential model building, residual fitting, and the Gradient Boosting framework |
| 4 | Introduction to XGBoost | Regularized gradient boosting, tree pruning, and performance optimizations |
| 5 | LightGBM and CatBoost | Leaf-wise growth, categorical feature handling, and efficient large-scale boosting |
| 6 | Handling Imbalanced Data | Resampling techniques, class weighting, and evaluation metrics for skewed datasets |
| 7 | Ensemble Learning Project | End-to-end project comparing ensemble models on a real-world dataset |
- Build an intuitive and practical understanding of ensemble learning techniques
- Implement bagging- and boosting-based algorithms from the ground up and using popular libraries
- Compare the strengths, trade-offs, and use cases of Random Forests, Gradient Boosting, XGBoost, LightGBM, and CatBoost
- Apply strategies for handling imbalanced datasets in real-world scenarios
- Consolidate learning through a comparative project on a real dataset
- Language: Python
- Libraries: scikit-learn, XGBoost, LightGBM, CatBoost, pandas, NumPy, Matplotlib/Seaborn
- Environment: Jupyter Notebook
Machine-Learning-Algorithms/
│
├── 01_Introduction_to_Ensemble_Learning/
├── 02_Bagging_and_Random_Forests/
├── 03_Boosting_and_Gradient_Boosting/
├── 04_Introduction_to_XGBoost/
├── 05_LightGBM_and_CatBoost/
├── 06_Handling_Imbalanced_Data/
├── 07_Ensemble_Learning_Project/
└── README.md
- Clone the repository
git clone https://github.com/<your-username>/Machine-Learning-Algorithms.git cd Machine-Learning-Algorithms
- Install dependencies
pip install -r requirements.txt
- Launch Jupyter Notebook and explore the folders in order
jupyter notebook
The final module, Ensemble Learning Project, brings together everything covered in the repository — applying and benchmarking Random Forests, Gradient Boosting, XGBoost, LightGBM, and CatBoost on a real dataset, with a comparative analysis of accuracy, training time, and robustness to imbalanced classes.
This project is open source and available under the MIT License.
Contributions, suggestions, and improvements are welcome. Feel free to open an issue or submit a pull request.