{"id":24464228,"url":"https://github.com/khushi130404/ensemble-learning","last_synced_at":"2025-07-31T03:36:57.074Z","repository":{"id":270227176,"uuid":"909673517","full_name":"Khushi130404/Ensemble-Learning","owner":"Khushi130404","description":"This project demonstrates various ensemble learning techniques using Jupyter Notebook. 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The goal is to explore how ensemble methods can improve the performance of machine learning models for both regression and classification tasks.\n\n## Techniques Covered\n\nThe following ensemble methods have been implemented and evaluated in this project :\n- Adaboost (Adaptive Boosting)\n- Bagging (Bootstrap Aggregating)\n- Gradient Boosting\n- Random Forest\n- Stacking Ensemble\n- Voting Ensemble\n\n## Problem Types\n\nThe project includes implementations for :\n\n- Regression Problems\n\n- Classification Problems\n\n## Project Structure\n\nThe project is organized into the following sections :\n\n### 1. Data Preprocessing\n   \n- Loading datasets\n- Handling missing values\n\n### 2. Feature scaling\n   \n- Model Implementation\n- Implementing each ensemble technique for regression and classification tasks\n\n### 3. Model Evaluation\n\n- Comparing model performance using metrics such as:\n  - For Regression: Mean Squared Error (MSE), R-Squared (R²)\n  - For Classification: Accuracy, Precision, Recall, F1-Score\n\n## Ensemble Methods Overview\n\n### 1. Adaboost (Adaptive Boosting)\n\n- Works by combining multiple weak classifiers to create a strong classifier.\n- Adjusts the weights of incorrectly classified instances to focus on difficult cases.\n\n### 2. Bagging (Bootstrap Aggregating)\n\n- Reduces variance by training multiple models on different subsets of the dataset.\n- Combines predictions through averaging (for regression) or majority voting (for classification).\n\n### 3. Gradient Boosting\n\n- Builds models sequentially, with each new model correcting the errors of the previous ones.\n- Suitable for both regression and classification tasks.\n\n### 4. Random Forest\n\n- An extension of bagging that uses decision trees as base learners.\n- Introduces randomness by selecting a random subset of features for each split.\n\n### 5. Stacking Ensemble\n\n- Combines multiple models (base learners) by training a meta-model to make final predictions.\n- Allows using different types of models as base learners.\n\n### 6. Voting Ensemble\n\n- Combines predictions from multiple models by voting (for classification) or averaging (for regression).\n- Can be hard voting (majority vote) or soft voting (weighted probabilities).\n\n## Tools and Libraries Used\n\n- Jupyter Notebook for interactive code execution\n\n- scikit-learn for implementing ensemble methods\n\n- pandas for data manipulation\n\n- numpy for numerical computations\n\n- matplotlib and seaborn for data visualization\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fensemble-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkhushi130404%2Fensemble-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fensemble-learning/lists"}